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The purpose of the study is to mainstream the disaster risk reduction (DRR) and disaster resilience measures into the GreenSL® building rating system in Sri Lanka to demonstrate a more holistic approach in achieving sustainability with resilience.

First, a comprehensive literature review was carried out on green building practices and rating tools, natural hazards and DRR and global and local frameworks. Next, criteria were developed incorporating the disaster resilience aspects through eight expert committee meetings and included the rating tool in three levels. Finally, 11 green-certified buildings from the Green Building Council of Sri Lanka were reevaluated using the new disaster resilient GREENSL® rating system for built environment as a validation for the new tool and to analyze the new certification levels.

All the green-certified buildings in hazard-prone areas dropped their certification by one level while the buildings in nonhazard-prone areas remained in the certification after being evaluated by the new DRR-incorporated rating system, which recommends the use of new tool for the buildings in hazard-prone areas. But due to the rapid changes in climate and the unpredictable trends of natural hazards, the use of new DRR incorporated GREENSL® rating system for built environment for the certification of buildings is highly endorsed.

This is the first research study based on the GREENSL rating system. And it is the first effort to incorporate DRR aspects to any rating tool in Sri Lanka. The projects that were evaluated for the validation of the new tool are the actual projects which obtained green building certification. Also, it is apparent that this study has gone some way towards enhancing the understanding of blending the sustainability and resilience in rating systems for built environment.

The purpose of this paper is to present a methodology for assessing, in quantifiable terms, the reduction in environmental impacts achieved by applying different credits of sustainability rating systems in building construction projects.

Sustainability rating systems are developed in various regions to evaluate construction projects with respect to their environmental performance. Although implementation of rating systems had a recognized effect on reducing environmental impact of construction projects, there is no objective and quantifiable evidence that the approaches recommended by these rating systems to achieve the required certification lead to optimum environmental results. This paper presents a methodology that utilizes life cycle analysis (LCA) as a powerful and objective tool to validate the way rating systems evaluate project performance. The Qatar Sustainability Assessment System (QSAS), recently developed in the State of Qatar by Gulf Organization for Research and Development (GORD), is chosen as a case study to illustrate application of the developed methodology. Environmental impacts due to implementation of QSAS credits are calculated for one project in Qatar, which is currently under construction.

Results reveal possible use of LCA as a tool for evaluating the effectiveness of rating systems. For the QSAS case study, findings reveal indications of over and, in some instances, under estimation of the weights assigned to some credits and the difficulty in the quantification of the impacts of other credits, which indicates the need for reconsideration of these weights to improve effectiveness of the implementation of these credits.

The proposed methodology stands as a step toward the enhancement and rationalization of the currently used building sustainability ratings system.

The purpose of this paper is to propose a system with the highest discriminatory power by selecting an indicator system based on the K–S test according to the unique circumstances of small enterprises.

The proposed method relies on calculating the K–S test statistical magnitude of D iteratively to reach a system with the maximum discriminatory power.

The empirical results, demonstrated using 3,045 small businesses from a Chinese bank, show that credit rating system should focus on the indicator system’s discriminatory power rather than a single indicator’s discriminatory power, because the interaction between indicators affects the discriminatory power of the system.

The proposed method creates a credit rating system with the highest discriminatory power, rather than its indicators, which is a more reasonable and novel approach to credit rating.

The approach is unique because the final system will have high discriminatory power and has excellent potential for decision support. The authors believe that this contribution is theoretically and practically relevant because credit rating for small business is especially difficult and complicated.

Recent years have witnessed the development of a variety of rating systems but the authors have little knowledge about their impact on users’ perceptions of information quality, cognitive decision effort, and enjoyment. The purpose of this paper is to understand the potential cognitive fit underlining the relationship between rating systems types (i.e. five-star, binary-visual, and binary-textual) and tasks (i.e. purchase-decision and browsing tasks) in the context of shopping websites.

A total of 191 subjects were obtained. This study conducted an experiment with a 2×3 between-subject factorial design. The first dimension is a task that has two conditions (purchasing vs browsing). The second dimension is a type of rating system that has three different types (binary-textual, binary-visual, and five-star).

The results show that the cognitive fit may occur when individuals use a five-star rating system to help them make a purchasing decision and when they use a binary-visual rating system while browsing. This fit might increase perceived information quality while decrease cognitive decision efforts, and in turn raise intention to adopt the systems. Moreover, five-star rating systems can make users feel more fun and enjoyment than binary-textual and visual rating systems, regardless of task type.

This study focuses on three main rating systems that are popular on shopping websites. Nevertheless, few other rating systems exist in the market such as unitary or ten-star rating systems. Further studies can consider other types of rating systems and address, in addition to representation, the issue of information granularity (i.e. unitary, binary, five-star, and ten-star rating systems).

The results of this study could provide design principles for web designers in determining which rating systems best match the websites they are developing. If the websites or specific webpages are more utilitarian oriented (hedonic oriented), five-star rating systems (binary-visual rating systems) are more appropriate.

This study contributes to the rating system literature by examining the cognitive fit underlining the relationship between rating systems types and tasks. Importantly, this study extends cognitive fit theory by considering affective responses, that is, perceived enjoyment and intention to continue to use.

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.

The purpose of this paper is to propose a debt rating index system for small industrial enterprises that significantly distinguishes the default state. This debt rating system is constructed using the F-test and correlation analysis method, with the small industrial enterprise loans of a Chinese commercial bank as the data sample. This study establishes the weighting principle for the debt scoring model: “the more significant the default state, the larger is the weight.” The debt rating system for small industrial enterprises is constructed based on the standard “the higher the debt rating, the lower is the loss given default.”

In this study, the authors selected indexes that pass the homogeneity of variance test based on the principle that a greater deviation of the default sample’s mean from the whole sample’s mean leads to greater significance in distinguishing the default samples from the non-default samples. The authors removed correlated indexes based on the results of the correlation analysis and constructed a debt rating index system for small industrial enterprises that included 23 indexes.

Among the 23 indexes, the weights of 12 quantitative indexes add up to 0.547, while the weights of the remaining 11 qualitative indexes add up to 0.453. That is, in the debt rating of the small industry enterprises, the financial indexes are not capable of reflecting all the debt situations, and the qualitative indexes play a more important role in debt rating. The weights of indexes “X17 Outstanding loans to all assets ratio” and “X59 Date of the enterprise establishment” are 0.146 and 0.133, respectively; both these are greater than 0.1, and the indexes are ranked first and second, respectively. The weights of indexes “X6 EBIT-to- current liabilities ratio,” “X13 Ratio of capital to fixed” and “X78 Legal dispute number” are between 0.07 and 0.09, these indexes are ranked third to fifth. The weights of indexes “X3 Quick ratio” and “X50 Per capital year-end savings balance of Urban and rural residents” are both 0.013, and these are the lowest ranked indexes.

The data of index i are divided into two categories: default and non-default. A greater deviation in the mean of the default sample from that of the whole sample leads to greater deviation from the non-default sample’s mean as well; thus, the index can easily distinguish the default and the non-default samples. Following this line of thought, the authors select indexes that pass the F-test for the debt rating system that identifies whether or not the sample is default. This avoids the disadvantages of the existing research in which the standard for selecting the index has nothing to do with the default state; further, this presents a new way of debt rating. When the correlation coefficient of two indexes is greater than 0.8, the index with the smaller F-value is removed because of its weaker prediction capacity. This avoids the mistake of eliminating an index that has strong ability to distinguish default and non-default samples. The greater the deviation of the default sample’s mean from the whole sample’s mean, the greater is the capability of the index to distinguish the default state. According to this rule, the authors assign a larger weight to the index that exhibits the ability to identify the default state. This is different from the existing index system, which does not take into account the ability to identify the default state.

In recent years, practitioners have identified a number of problems with traditional performance management (PM) systems, arguing that PM is broken and needs to be fixed. In this chapter, we review criticisms of traditional PM practices that have been mentioned by journalists and practitioners and we consider the solutions that they have presented for addressing these concerns. We then consider these problems and solutions within the context of extant scholarly research and identify (a) what organizations should do going forward to improve PM practices (i.e., focus on feedback processes, ensure accountability throughout the PM system, and align the PM system with organizational strategy) and (b) what scholars should focus research attention on (i.e., technology, strategic alignment, and peer-to-peer accountability) in order to reduce the science-practice gap in this domain.

This paper aims to propose a trust-based ant-colony recommender system. It achieves high accuracy and coverage by integrating the importance level of friends. This paper has two main contributions, namely, selecting higher-quality raters and improving the prediction step. From these two contributions, the proposed trust-based ant-colony recommender system could provide more accurate and wider-coverage prediction than existing systems.

To obtain higher-quality raters, the data set was preprocessed, and then, trust values were calculated. The depth of search was increased to obtain higher coverage levels. This work also focuses on the importance level of friends in the system. Because the levels of influence on the active user of all friends are not equal, the importance level of friends is integrated into the system by transposing rater’s rating to the active user’s perspective and then assigning a weight to each rater.

The experimental evaluation clearly demonstrates that the proposed method achieves better results in terms of both accuracy and coverage than existing trust-based recommender systems. It was found that integrating the importance level of friends into the system, which transposes ratings and assigns weight to each user, can increase accuracy and coverage.

Existing trust-based ant-colony recommender systems do not consider the importance level of friends in the prediction step. Most of them only focus on finding raters and then using the rater’s real ratings in the prediction step. A new method is proposed that integrates the importance level of friends into the system by transposing a rater’s rating to match the active user’s perspective and assigning a weight for each rater. The experimental evaluation demonstrates that the proposed method achieves better accuracy and coverage than existing systems.

With the increasing demand for sustainable developments, higher education should focus on teaching both sustainable buildings and infrastructure systems. This study aims to investigate the existing sustainable infrastructure (SI) teaching efforts in sustainability courses; identify best practices to integrate SI throughout bachelor’s and master’s programs under the construction management (CM) curricula; and propose guidelines for students to obtain Envision sustainability professional (ENV SP) credential during sustainability education.

This study conducted keywords search within the sustainability course descriptions under the American Council for Construction Education accredited CM curricula to locate SI topics. Additionally, this research collected inputs from the Envision Academic Committee members to develop a matrix representing the best practices for integrating SI in higher education and provided a guide with a step-by-step procedure to obtain ENV SP credentials.

This study identified a gap regarding the availability of SI education and offered best practices on how CM curricula might nurture such knowledge. Phase I highlighted that only two CM programs taught infrastructure sustainability, and three programs offered sustainability credentialing processes under a bachelor’s degree. Phase II developed a framework that offered a variety of pedagogical approaches and outlined the process for obtaining the ENV SP certificate for CM students in the freshman, sophomore, junior and senior years.

The findings of this study can facilitate CM education to create awareness among the future workforce and encourage them to establish skills pertaining to the economic, social and environmental implications while designing SI.