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The purpose of this paper is to examine the potential effectiveness of government reforms aimed at improving the accuracy of ratings issued by credit ratings agencies in US financial markets.

The paper identifies unconscious bias as a source of inaccuracy in the credit ratings process. It examines prior behavioral research on unconscious bias, and uses this research to identify structural issues within the credit ratings industry that give rise to biased judgments. Finally, it examines whether government reforms will be effective in improving the accuracy of credit ratings, and offers additional reforms aimed at combating unconscious bias.

Recent government reforms will be most effective in curbing intentional decisions to compromise the ratings process. However, the reforms will be less effective at mitigating unconscious biases in judgments underlying credit ratings, because they do not adequately address relevant structural issues. To combat unconscious bias, changes need to be made to ratings agencies' fee structures, business models, and risk management functions.

The analysis is of use to regulators who are contemplating the need for reforms aimed at improving the accuracy of credit ratings. While focusing on events in the USA, the analysis is relevant to any country in which credit ratings are influential in financial markets.

This is the first paper to examine the performance of credit ratings agencies through the lens of behavioral psychology, and to introduce the concept of unconscious bias as a determining factor in the accuracy of credit ratings.

The objective of this empirical study is to apply the methodology commonly used to performance appraisal and examine if outcomes achieved by ratees bias rater's judgment of ratee ethical behavior.

Two studies were conducted: in study 1 the participants were undergraduate business students and in study 2, the participants were MBA students but who were also full time employees. In both these studies, participants read the vignettes and rated the ratee performance using behavior observation scale.

Both the studies found support for the main hypothesis that outcomes achieved by the ratees influenced judgment of ethical behavior. The hypothesis that ethical beliefs of raters will moderate the biasing influence of outcomes on ethical judgment bias was not supported.

If outcomes achieved by employees influence judgment of ethical behavior, future research has to examine how the biasing influence of outcomes on ethical judgments can be mitigated or eliminated.

If managers are influence by outcomes achieved by their employees in judging the ethical behavior, it can lead to “success breeds acceptance” culture. If organizations place undue emphasis on outcomes at the cost of ethical standards, unethical behavior of individuals could be condoned or justified which would lead to worsening of ethical climate in these organizations.

This study demonstrated that outcomes achieved by employees biases judgment of their ethical behavior and this finding has important implications for designing effective appraisal systems for assessing ethical behavior of employees.

Academic research has supported the belief that consumers undertip minority race service workers due to implicit racial biases. However, there has been less focus in examining possible moderating factors. This paper aims to fill this gap by analyzing the role of direct and indirect experience in tipping frontline service workers from a minority background. Given the prominence of customer ratings on digital service platforms and the perception that African Americans are discriminated against, the authors look at the interplay of interaction length (direct experience) and customer ratings (indirect experience) on the relationship between race and tipping.

An expectancy disconfirmation framework was developed and tested with a sample of 360 US participants in an online experiment. The experiment followed a 2 × (race: African-American versus Caucasian) × 2 (direct experience: limited versus extensive) × 3 (indirect experience: absent versus positive versus negative customer rating) design.

The authors found consumers who have extended direct experience (longer service interaction) and no indirect experience (absent customer ratings) tipped African Americans more than Caucasians. Interestingly, this effect is reduced in the presence of indirect experience (customer ratings). Finally, where the consumer lacks direct experience (shorter service interaction) but is exposed to positive indirect experience (positive customer ratings), consumers tip African Americans more.

To the best of the authors’ knowledge, this is the first paper that examines the role of direct and indirect experience in the relationship between race and tipping. Based on the authors’ findings, the authors provide several contributions, including recommendations to reduce inequalities arising from implicit racial bias on digital service platforms.

University students' ratings of teaching and teachers' performance are used in many parts of the world for the evaluation of faculty members at colleges and universities. Even though these ratings receive mixed reviews, there is little conclusive evidence on the role of the intervening variable of teacher and student gender in these ratings. Possible influences resulting from gender-related differences in different socio-cultural contexts, especially where gender combination in student and faculty population is not proportionate, have not been adequately investigated in previous research. This study aimed to examine Iranian university students' ratings of the professional performance of male and female university teachers and to explore the differences in male and female university students' evaluation of teachers of the same or opposite gender. The study was a questionnaire-based cross-sectional survey with a total of 800 randomly selected students in their different years of undergraduate study (307 male and 493 female students, reflecting the proportion of male and female students in the university) from different faculties at the University of Kashan, Iran. The participants rated male and female teachers’ performance in observing university regulations, relationship with colleagues, and relationships with students. The researchers used descriptive statistics, means comparison inferential statistics and focus-group interview data to analyze and compare the students’ ratings. The results of one-sample t-test, independent samples t-test, and Chi-square analyses showed that a) overall, male university teachers received significantly higher overall ratings in all areas than female teachers; b) male students rated male teachers significantly higher than female students did; and c) female students assigned a higher overall mean rating to male teachers than to female teachers but this mean difference was not significant. These results are studied in relation to the findings in the related literature and indicate that gender can be an important intervening variable in university students' evaluation of faculty members.

This paper reports results of analyses made at an all-female Gulf Arab university measuring the nature and extent of biases in students' evaluation of faculty. Comparisons are made with research reporting the nature of similar relationships in North America. Two issues are investigated: 1) What variables (if any) bias faculty evaluation results at an all-female Arab university? 2) Are biasing variables different in nature or magnitude to those reported at North America universities? Using the population of 13,300 faculty evaluation records collected over two school years at Zayed University, correlations of faculty evaluation results to nine potentially biasing factors are made. Results show biases to faculty evaluation results do exist. However, biases are small, and strikingly similar in nature to those reported at North American universities.

The purpose of this paper is to propose a new approach to talent management that consists of averaging performance appraisal and assessment center ratings for in-depth identification of lawyers’ talents.

The approach’s adjustment was examined using a 61 senior-lawyer sample from a Portuguese law firm. Comparisons between assessment center and performance appraisal ratings were analyzed using paired-sample t-tests and a kernel density function, and predictive validity was assessed with Pearson correlations. Evidence of both a general performance factor and two additional factors was verified using principal component analysis. Varimax rotation was used to verify three broad factors with job profile’s three broad areas.

Results suggest support for the assessment center’s predictive validity. Its lower and more variable ratings overcome performance appraisal rating bias. Adjustment of the new approach to lawyers’ overall talent identification (the general factor) and each lawyer’s relative talents (three broad factors) was observed.

This study contributes to the body of knowledge regarding the substantive existence of a general performance factor, and adds to empirical research concerning talent management, which is lacking. However, generalizability requires broader samples and replication.

The approach is a methodology that informs career management, high-flyers’ identification, talent mapping, development, succession planning, team composition, and diversity analysis. For lawyers, objective feedback allows benchmarking talent and managing one’s career.

This study pioneers empirical research that develops methods for identifying talent in law firms, vital for firm sustainability.

The purpose of this paper is to contribute to the empirical evidence about crop yield distributions that are often used in practical models evaluating crop yield risk and insurance. Additionally, a simulation approach is used to compare the performance of alternative specifications when the underlying form is not known, to identify implications for the choice of parameterization of yield distributions in modeling contexts.

Using a unique high-quality farm-level corn yield data set, commonly used parametric, semi-parametric, and non-parametric distributions are examined against widely used in-sample goodness-of-fit (GOF) measures. Then, a simulation framework is used to assess the out-of-sample characteristics by using known distributions to generate samples that are assessed in an insurance valuation context under alternative specifications of the yield distribution.

Bias and efficiency trade-offs are identified for both in- and out-of-sample contexts, including a simple insurance rating application. Use of GOF measures in small samples can lead to inappropriate selection of candidate distributions that perform poorly in straightforward economic applications. The β distribution consistently overstates rates even when fitted to data generated from a β distribution, while the Weibull consistently understates rates; though small sample features slightly favor Weibull. The TCMN and kernel density estimators are least biased in-sample, but can perform very badly out-of-sample due to overfitting issues. The TCMN performs reasonably well across sample sizes and initial conditions.

Economic applications should consider the consequence of bias vs efficiency in the selection of characterizations of yield risk. Parsimonious specifications often outperform more complex characterizations of yield distributions in small sample settings, and in cases where more demanding uses of extreme-event probabilities are required.

The study helps provide guidance on the selection of distributions used to characterize yield risk and provides an extensive empirical demonstration of yield risk measures across a high-quality set of actual farm experiences. The out-of-sample examination provides evidence of the impact of sample size, underlying variability, and region of the probability measure used on the performance of candidate distributions.

This research aims to investigate the differences in evaluations of job performance between male and female managers by those managers’ immediate bosses and peers.

Drawing on gender structure theory, along with ideas about status characteristics, the authors use hierarchical regression to test the hypotheses that male and female bosses and peers deferentially evaluate the male and female manager’s global job performance. The authors hypothesize significant two-way interactions (gender of the manager by gender of evaluator) in predicting a manager’s job performance.

The results suggest that while male peers rate female managers’ job performance significantly lower than that of male managers, female peers do not discriminate between genders in their performance evaluations. Also, managers’ bosses were found not to discriminate between genders of their subordinates.

The limitations of this study have to do primarily with the data. While the data are rich on some dimensions, they are weak on others, especially with regard to the detail about the jobs the respondents did, detailed level of familiarity with the evaluated managers, as well as racial background. The data also do not provide information on the different facets of job performance, the evaluation of which could potentially be impacted by managerial gender; this study is focused exclusively on global job performance.

The authors discuss various theoretical explanations of this pattern of results, as well as its possible influence on female managers’ careers. Although the effect size of the negative bias that male peers exhibit toward female managers is relatively small, it may be argued that lower performance assessments can accumulate over years in multiple job evaluations, negatively affecting the career of female leaders.

The evaluations supplied by different organizational members gain importance with the increased use of 360-degree feedback instruments not just for developmental but also for the job performance appraisal purposes. While the job evaluations of managers’ bosses have been investigated in the past with regard to the possible gender bias, this study provides the first known to the authors’, evidence. Also, this study points to a direct bias in performance assessments, rather than a potentially more subtle, non-performance-based bias that affects the disparities in wages and promotions of female managers. Thus, this study helps to fill a significant gap in the literature on organizations and it may have practical implications for the advancement of female managers. In addition to this contribution, this study also provides data that may be useful in resolving the ongoing debate whether female bosses act more as cogs in the machine or as change agents in organizations.

In order to determine the usefulness of multi‐source rating in different types of organizations, this study explored differences among organization types in four areas: leniency, interrater agreement, relationships between these ratings and effectiveness, and the relationship between agreement and effectiveness. Used self, subordinate, peer, and supervisor ratings for 1,080 target managers in six types of organizations: education, military, government, manufacturing, finance, and health. Interrater agreement was measured in three ways: an index of variance, a point‐difference categorization method, and categories of self‐other agreement. Results indicated that a leniency bias was present in educational institutions, after controlling for demographic characteristics. Interrater agreement was lowest in government agencies and highest in education and manufacturing organizations. In private sector organizations, more poor‐performing managers tended to over‐estimate their performance relative to the perceptions of others. Interrater agreement was positively related to effectiveness especially in education and finance organizations. Results suggest that multi‐source feedback may work differently in different types of organizations, and such differences may need to be taken into account by researchers, practitioners, and feedback recipients.

This paper aims to study the determinants of the sovereign debt ratings provided by the 3 main rating agencies for 32 European countries. It verifies the clusters of countries existing for each of the agencies, considering regional bias, and then analyzes whether the determinants were different before and after the global financial crisis. It also aims to explain how the determinants are taken into account for rich and developing countries, using a sample for the period between 2001 and 2008 and the period between 2009 and 2016.

To this purpose, this paper performs panel data estimation using an ordered Probit approach.

This method shows that for developing countries after the crisis, the relevant explanatory variables are the unemployment rate and the presence in the Eurozone. For rich countries, the inflation rate is pivotal after the crisis period.

This paper is the first to use a clustering methodology within sovereign debt rating literature, grouping the countries into cohesive clusters according to their sovereign debt ratings along with the proposed time frame. Moreover, it explains, which countries belong to strong or weak groups, according to the rating agencies under discussion; and, in these groups, it identifies the sovereign rating determinants.