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Managers frequently complain that performance ratings are inflated; thus, this study aims to explore what extent two motivational factors theoretically associated with accountability, rating audience and incentive, can influence rating inflation.

One hundred and forty‐nine raters were assigned to one of four audience conditions (ratee, expert, both ratee and expert – dual, and no audience) and either to an incentive or no incentive condition.

Results showed that when an incentive was offered, raters expecting an expert audience to view their ratings provided significantly lower ratings, and raters expecting a dual audience provided significantly higher ratings compared to raters not offered an incentive. Furthermore, raters expecting a ratee audience inflated their ratings, regardless of incentive.

Financial incentives were used in this study and more research is needed to explore other types of incentives. Nonetheless, this research shows that incentives influence rating level.

The research suggests that if managers wish to reduce rating inflation, they should ensure that an audience, other than the person being rated, will view the ratings.

This study is the first to show that feelings of accountability and rating level are influenced by incentives, and that the audience of the ratings can determine whether incentives result in lower or higher ratings. Furthermore, it appears that the tendency to inflate ratings given a ratee audience may be quite powerful, even in the absence of specific incentives.

Expert wine awards are commonly used by consumers to reduce complexity in wine choice but little is known about expert vs non‐expert perceptions of sensory wine quality. This paper aims to examine if expert ratings are suitable quality indicators for consumers and whether there are certain groups of consumers that find expert awards more useful than others.

The paper compares German consumer ratings obtained in a sensory laboratory with German Agricultural Society's quality competition awards. it tests for the correspondence between expert and non‐expert ratings and for the concordance within the non‐expert group. Estimation of a linear mixed model serves to identify consumer‐side variables with an influence on individual rating distance.

Correspondence between expert and non‐experts and concordance within the non‐expert group were found to be insignificant. Experienced wine consumers with sufficient specific knowledge and superior self‐reported sensory skills better replicated expert ratings.

With 216 wine ratings obtained from 36 German consumers, the number of observations is small. Future research should verify above findings by considering more consumers and the stability of ratings across time.

The findings suggest that although some consumer segments may find expert awards to be useful decision cues, for a large portion of the market, there is demand for a more consumer‐orientated system of sensory quality evaluation and labelling.

This paper is the first to address the usefulness of expert ratings to novice and experienced wine consumer populations. The statistical procedures employed (including linear mixed modelling) are shown to be useful techniques to handle the repeated measurement nature of the data.

This study aims to explore the objectivity in third-party ratings. Third-party ratings are often based on some form of aggregation of various experts' opinions with the assumption that the potential judgment biases of the experts cancel each other out. While psychology research has suggested that experts can be unintentionally biased, management literature has not considered the effect of expert bias on the objectivity of third-party ratings. Thus, this study seeks to address this issue.

Ranking data from the US News and World Report between 1993 and 2008, institution-related variables and, to represent sports prominence, NCAA football and basketball performance variables are leveraged in testing our hypotheses. A mediating-model is tested using regression with panel-corrected standard errors.

This study finds that the judgments of academicians and recruiters, concerning the quality of universities, have been biased by the prominence of a university's sports teams and that the bias introduced to these experts mediates the aggregated bias in the resultant rankings of MBA programs. Moreover, it finds that experts may inflate rankings by up to two positions.

This study is particularly relevant for university officials as it uncovers how universities can tangibly manipulate the relative perception of quality through sports team prominence. For third-party rating systems, the reliability of ratings based on aggregated expert judgments is called into question.

This study addresses a significant gap in the literature by examining how a rating system may be unintentionally biased through the aggregation of experts' judgments. Given the heavy reliance on third-party rating systems by both academics and the general population, addressing the objectivity of such ratings is crucial.

To explain and empirically test how different marketing communication channels interact with each other and contribute to brands’ diverging marketplace performance.

With a unique data set combining key variables of major passenger car brands, the paper takes a source-based perspective to investigate how firm-based communications, expert opinions and online consumer reviews interact and affect brands’ marketplace performance. Then the paper studies the three special boundary conditions under which online consumer reviews’ influence varies in competition with the other two established information sources. Lastly, a study was done to demonstrate the financial significance of investing in different information sources.

The results show that online consumer reviews mitigate the effectiveness of the other two information sources in driving brand sales. This mitigation effect is also magnified when the brand is weak, firm-based communications are modest and expert opinions are less favorable. The findings further suggest that in the emerging communication enterprise, firm-based and expert-based communications remain the core while user-based communication plays an indispensable competing and complementary role.

In the new digital era, firms are facing the daunting task of understanding and integrating multiple communication channels. The study provides important implications for both researchers and practitioners with respect to brand management and integrated communications.

Existing studies have demonstrated that each of the three communication efforts (by firms, experts and consumers) exerts a significant influence on product sales, but few studies have been conducted in settings marked by the coexistence of these efforts. In addition, the three communication efforts are likely to have different effects on brands with different market positions. The current study is contributing to the literature by filling the above gaps.

The growing recognition of the role of teacher innovative behavior in educational improvement has led to more systematic assessment of teacher-driven innovations, usually through expert panels. Innovative peer-teachers may be more closely aligned with the correlates of teacher innovative behavior than experts, and hence their participation in such panels might make the process more robust. Hence, the authors ask, “Do expert and peer assessments relate to individual-related correlates of innovative teacher behavior differently?”

Innovations of 347 teachers in India were assessed by an expert panel and a peer-teacher panel using the consensual technique of rating innovations. Structural equation modeling was used to study the relationships of the ratings with the innovative teachers' self-reported creative self-efficacy, intrinsic motivation, learning orientation and proactive personality.

Expert ratings were significantly related to creative self-efficacy beliefs (β = 0.53, p < 0.05), whereas peer ratings were not. Peer ratings were significantly related to learning orientation (β = 0.19, p < 0.05), whereas expert ratings were not. Also, expert ratings were found to be indirectly associated with teachers' proactive personality and intrinsic motivation via creative self-efficacy beliefs; peer ratings were not associated with proactive personality.

The paper, through a robust methodology that relates expert and peer assessments with individual-related correlates of innovative behavior, makes a case for educational innovation managers to consider mixed panels of experts and innovative teacher-peers to make the assessment process more robust.

This paper aims to provide insight into the three most prevalent expert wine rater sources and how they separately affect retail prices post-release across a sample of French and US wines from the 2012 vintage.

Empirical research using regression models built on data scraped from Web sources provides the source for the substance of the paper.

The findings suggest that all rating sources affect release price (approximately $3-4 per point), but more indicative of market performance, only Wine Advocate ratings significantly influence price change in the market post-release. Other results suggest some, but far from complete, consistency among raters. Red wines and French wines typically fetch better scores from the raters, and they are less subject to price drops in the marketplace.

The nature of the data does not allow for consumers’ individual difference characteristics, such as wine knowledge, among others, to be included as potential factors explaining why and when expert ratings influence consumers.

Third-party wine ratings do indeed matter both in terms of release price and post-release price performance. In particular, following release, Wine Advocate ratings provide the most influential quality signal in the marketplace.

Scrutiny on the manner in which ratings information is used by retailers is appropriate, given the influence such ratings have on consumers as demonstrated by their effects on market behaviors.

The research examines the top three US expert ratings and considers their consistency and impact, not just on release price but also on price following release, as a direct indicator of product performance in the marketplace.

The purpose of this paper is to compare the acceptance ratings and drivers of liking and disliking attributes of aseptic-packaged cooked rice by consumers, researchers and experts.

Descriptive analysis (DA) was conducted using trained panelists. Acceptability was measured by consumers, researchers and experts. The results of DA and acceptability were analyzed using partial least square regression.

There was no strong relationship among the three groups in their rating patterns for the samples (r=−0.342-0.445). The liking factors for each group were as follows: consumers (rice cake flavor and moisture), researchers (wet wood flavor and whiteness) and experts (wet wood flavor and size of rice). The disliking factors for each group were as follows: consumers (wet wood flavor and brown particle), researchers (moisture) and experts (old rice aroma). The consumers, researchers and experts seemed to have different acceptances and key descriptive attributes for aseptic-packaged cooked rice.

The consensus by researchers during the product development process required caution with regard to the fact that the evaluation by the researchers could be different from what consumers or experts prefer.

Setting-up in-house panelists group would be minimized the discrepancy between consumers and researches.

This study contributes to understanding of the acceptability by food researchers and comparing to consumers and experts for the first time in sensory field.

A good recommender system helps users find items of interest on the web and can provide recommendations based on user preferences. In contrast to automatic technology‐generated recommender systems, this paper aims to use dynamic expert groups that are automatically formed to recommend domain‐specific documents for general users. In addition, it aims to test several effectiveness measures of rank order to determine if the top‐ranked lists recommended by the experts were reliable.

In the approach, expert groups evaluate web documents to provide a recommender system for general users. The authority and make‐up of the expert group are adjusted through user feedback. The system also uses various measures to gauge the difference between the opinions of experts and those of general users to improve the evaluation effectiveness.

The proposed system is efficient when there is major support from experts and general users. The recommender system is especially effective where there is a limited amount of evaluation data from general users.

This is an original study of how to effectively recommend web documents to users based on the opinions of human experts. Simulation results were provided to show the effectiveness of the dynamic expert group for recommender systems.

The purpose of this paper is to discuss the case-based reasoning (CBR) approach to improve microcredit initiatives by means of providing a borrower risk rating system.

The CBR approach has been used to consider the Kiva microcredit system, which provides a characterization (rating) of the risk associated with the field partner supporting the loan, but not of the specific borrower which would benefit from it. The authors discuss how the combination of available historical data on loans and their outcomes (structured as a case base) and available knowledge on how to evaluate the risk associated with a loan request can be used to provide the end users with an indication of the risk rating associated with a loan request based on similar past situations.

The adopted approach is applied and evaluated employing a selection of cases from individual loans. From this perspective, the case base and the codified knowledge about how to evaluate risks associated with a loan represent two examples of knowledge IT artifacts.

The originality of the work lies in borrower risk rating in online indirect peer-to-peer microcredit lending platforms. The case base and the codified knowledge are the two contributions in knowledge IT artifacts.

Consulting experts is an effective way to utilize tacit resource. The purpose of the paper is to optimize the matching between panels of experts and groups of demanders to improve the efficiency of tacit knowledge sharing.

Experts and demanders express preferences using linguistic terms. The estimate method based on trust is developed to get missing ratings. Weights of demanders are determined and knowledge needs are identified. Three kinds of satisfaction are measured based on grey relational analysis. To maximize satisfaction of experts and demanders and safeguard meetings of knowledge needs as well as the workload of experts, the optimization model is constructed and the solution is optimal matching results.

The presented approach not only optimizes the matching between demanders and experts but also sets up a panel of experts in case that knowledge needs exceed a single expert’s capacity.

The approach expands research works of methods for tacit knowledge sharing. The continuous updating of matching results and the processing of the data with mixing formats need to be studied further.

The presented approach acts as a valuable reference for the development of knowledge management systems. It can be used in any scene that needs the match between experts and demanders.

The approach provides a new way of helping demanders to find appropriate experts. Both experts’ and demanders’ preferences are considered. A panel of experts is set up when needed. Expert resources are utilized more efficiently and knowledge needs are met more comprehensively.