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The paper's aim is to design and describe a new mathematical ware instrument allowing to facilitate group decision-making process with feedback.

The aim is achieved through the development of a method for achieving sufficient consistency of individual expert alternative rankings based on evolutionary algorithms. The method is targeted at minimizing the number of times the experts in the group are addressed.

The method developed and described in the paper allows to provide sufficient consistency level of individual expert rankings allowing to aggregate the rankings into a transitive preference relation.

The method is targeted at small expert groups. The method is limited by psycho-physiological constraints of human (expert's) mind, which is unable to analyze more than seven objects simultaneously.

The method can be used in different scopes of human activity requiring ordinal expert estimation.

The method is based on an original approach to organising feedback with experts. Genetic algorithm is used to determine the optimal candidate among the experts to be addressed at every feedback step.

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.

Current industry practices illustrate there is no standard method to estimate the number of hours worked on maintenance activities; instead, industry experts use experience to guess maintenance work hours. There is also a gap in the research literature on maintenance work hour estimation. This paper investigates the use of machine-learning algorithms to predict maintenance work hours and proposes a method that utilizes historical preventive maintenance order data to predict maintenance work hours.

The paper uses the design research methodology utilizing a case study to validate the proposed method.

The case study analysis confirms that the proposed method is applicable and has the potential to significantly improve work hour prediction accuracy, especially for medium- and long-term work orders. Moreover, the study finds that this method is more accurate and more efficient than conducting estimations based on experience.

The study has major implications for industrial applications. Maintenance-intensive industries such as oil and gas and chemical industries spend a huge portion of their operational expenditures (OPEX) on maintenance. This research will enable them to accurately predict work hour requirements that will help them to avoid unwanted downtime and costs and improve production planning and scheduling.

The proposed method provides new insights into maintenance theory and possesses a huge potential to improve the current maintenance planning practices in the industry.

The significant benefits associated with cruise tourism have mobilized port industry, as progressively, a large number of ports are developing cruise operations. Although increasing cruise traffic is a major goal for cruise ports, homeporting constitutes a strategic target of the majority of ports due to its greater economic benefits for both the port itself and its hinterland. The establishment of homeport traffic in a cruise port is subjected to a variety of port internal and external factors. Taking these into account, the paper aims at defining elements that affect the potential of a cruise port to become a homeport.

A sample of 47 Mediterranean ports is selected to form the basis for the implementation of an ordinal regression model which links the likelihood of ports to attract homeport traffic with seven explanatory variables which emerged from relevant literature and are split in the main categories of ports’ and hinterlands’ characteristics. To fit the model into the paper’s data, ports are divided into three categories based on their homeport cruise traffic.

The results of the empirical model signify that both internal and external factors affect the potential of a port to become a cruise homeport. Concerning the internal factors, adequate infrastructure allowing the facilitation of the last generation of cruise ships and the presence of a private enterprise in ports’ operation seems to foster homeport traffic. Additionally, efficiency in operations seems to be a crucial element. On the other hand, the connectivity of port’s; hinterlands, tourist infrastructure and the level of economic growth are proved to be the hinterlands’ elements which increase the likelihood of a port to attract additional homeport traffic.

The model forms a comprehensive evaluation basis for whether a cruise port should intensify its pursuit of homeport traffic, as the estimated coefficients could support port and local authorities to understand their competitive position against other ports and spot their strengths and weaknesses.

The paper contributes in the research dealing with the identification of crucial elements of homeporting from the port’s point of view. Although, it should be mentioned that previous efforts targeting on revealing the characteristics affecting the homeporting potential of ports mostly have been based on questionnaires and expert judgements or empirical models in which the total – and not the homeport traffic – was used as the dependent variable. With the proposed empirical model, home-porting choice analysis is transferred, on the one hand, from the stated preferences level to the revealed preferences level and, on the other hand, from an indirect to a direct approximation of the issue.

Factor analysis is the most used tool in organizational research and its widespread use in scale validations contribute to decision-making in management. However, standard factor analysis is not always applied correctly mainly due to the misuse of ordinal data as interval data and the inadequacy of the former for classical factor analysis. The purpose of this paper is to present and apply the Bayesian factor analysis for mixed data (BFAMD) in the context of empirical using the Bayesian paradigm for the construction of scales.

Ignoring the categorical nature of some variables often used in management studies, as the popular Likert scale, may result in a model with false accuracy and possibly biased estimates. To address this issue, Quinn (2004) proposed a Bayesian factor analysis model for mixed data, which is capable of modeling ordinal (qualitative measure) and continuous data (quantitative measure) jointly and allows the inclusion of qualitative information through prior distributions for the parameters’ model. This model, adopted here, presents considering advantages and allows the estimation of the posterior distribution for the latent variables estimated, making the process of inference easier.

The results show that BFAMD is an effective approach for scale validation in management studies making both exploratory and confirmatory analyses possible for the estimated factors and also allowing the analysts to insert a priori information regardless of the sample size, either by using the credible intervals for Factor Loadings or by conducting specific hypotheses tests. The flexibility of the Bayesian approach presented is counterbalanced by the fact that the main estimates used in factor analysis as uniqueness and communalities commonly lose their usual interpretation due to the choice of using prior distributions.

Considering that the development of scales through factor analysis aims to contribute to appropriate decision-making in management and the increasing misuse of ordinal scales as interval in organizational studies, this proposal seems to be effective for mixed data analyses. The findings found here are not intended to be conclusive or limiting but offer a useful starting point from which further theoretical and empirical research of Bayesian factor analysis can be built.

Our research aims in designation of a hybrid approach in the calibration of an attribute impact vector in order to guarantee its completeness in case when other approaches cannot ensure this.

Real estate mass appraisal aims at valuating a large number of properties by means of a specialised algorithm. We can apply various methods for this purpose. We present the Szczecin Algorithm of Real Estate Mass Appraisal (SAREMA) and the four methods of calibration of an attribute impact vector. Eventually, we present its application on the example of 318 residential properties in Szczecin, Poland.

We compare the results of appraisals obtained with the application of the hybrid approach with the appraisals obtained for the three remaining ones. If the database is complete and reliable, the econometric and statistical approaches could be recommended because they are based on quantitative measures of relationships between the values of attributes and properties' unit values. However, when the database is incomplete, the expert and, subsequently, hybrid approaches are used as supplementary ones.

The application of the hybrid approach ensures that the calibration system of an attribute impact vector is always complete. This is because it incorporates the expert approach that can be used even if the database excludes application of approaches that are based on quantitative measures of relationship between the unit real estate value and the value of attributes.

The purpose of this paper is to present an algorithm of real estate mass appraisal in which the impact of attributes (real estate features) is estimated by inequality restricted least squares (IRLS) model.

This paper presents the algorithm of real estate mass appraisal, which was also presented in the form of an econometric model. Vital problem related to econometric models of mass appraisal is multicollinearity. In this paper, a priori knowledge about parameters is used by imposing restrictions in the form of inequalities. IRLS model is therefore used to limit negative consequences of multicollinearity. In ordinary least squares (OLS) models, estimator variances might be inflated by multicollinearity, which could lead to wrong signs of estimates. In IRLS models, estimators efficiency is higher (estimator variances are lower), which could result in better appraisals.

The final effect of the analysis is a vector of the impact of real estate attributes on their value in the mass appraisal algorithm. After making expert corrections, the algorithm was used to evaluate 318 properties from the test set. Valuation errors were also discussed.

Restrictions in the form of inequalities were imposed on the parameters of the econometric model, ensuring the non-negativity and monotonicity of real estate attribute impact. In case of real estate, variables are usually correlated. OLS estimators are then inflated and inefficient. Imposing restrictions in form of inequalities could improve results because IRLS estimators are more efficient. In the case of results inconsistent with theoretical assumptions, the real estate mass appraisal algorithm enables having the obtained results adjusted by an expert. This can be important for low quality databases, which is often the case in underdeveloped real estate markets. Another reason for expert correction may be the low efficiency of a given real estate market.

This paper aims to deal with expert judgment and its predictive ability in the context of investment funds. The judgmental ratings awarder with a large set of experts was compared to a sample of the dynamic investment funds operating in Central and Eastern Europe with their objective performance, both past and future, relatively to the time of the forecast.

Data on the survey sample enabled the authors to evaluate both ex post judgmental validity, i.e. how the experts reflected the previous performance of funds, and ex ante predictive accuracy, i.e. how well their judgments estimated the future performance of the fund. For this purpose, logistic regression for past values estimations and linear model for future values estimations was used.

It was found that the experts (independent academicians, senior bank specialists and senior financial advisors) were only able to successfully reflect past annual returns of a five-year period, failing to reflect costs and annual volatility and, mainly, failing to predict any of the indicators on the same five-year horizon.

The outcomes of this paper confirm that expert judgment should be used with caution in the context of financial markets and mainly in situations when domain knowledge is applicable. Procedures incorporating judgmental evaluations, such as individual investment advice, should be thoroughly reviewed in terms of client value-added, to eliminate potential anchoring bias.

This paper sheds new light on the quality and nature of individual judgment produced by financial experts. These are prevalent in many situations influencing clients’ decision-making, be it financial advice or multiple product contests. As such, our findings underline the need of scepticism when these judgments are taken into account.

The purpose of this paper is to present how prior knowledge about the impact of real estate features on value might be utilised in the econometric models of real estate appraisal. In these models, price is a dependent variable and real estate features are explanatory variables. Moreover, these kinds of models might support individual and mass appraisals.

A mixed estimation procedure was discussed in the research. It enables using sample and prior information in an estimation process. Prior information was provided by real estate experts in the form of parameter intervals. Also, sample information about the prices and features of undeveloped land for low-residential purposes was used. Then, mixed estimation results were compared with ordinary least squares (OLS) outcomes. Finally, the estimated econometric models were assessed with regard to both formal criteria and valuation accuracy.

The OLS results were unacceptable, mostly because of the low quality of the database, which is often the case on local, undeveloped real estate markets. The mixed results are much more consistent with formal expectations and the real estate valuations are also better for a mixed model. In a mixed model, the impact of each real estate feature could be estimated, even if there is no variability in the sample information. Valuations are also more precise in terms of their consistency with market prices. The mean error (ME) and mean absolute percentage error (MAPE) are lower for a mixed model.

The crucial problem in econometric property valuation is that it involves the unreliability of databases, especially on undeveloped, local markets. The applied mixed estimation procedure might support sample information with prior knowledge, in the form of stochastic restrictions imposed on parameters. Thus, that kind of knowledge might be obtained from real estate experts, practitioners, etc.

Prior research addresses relationships between audit attributes and perceptions of both audit quality and auditee satisfaction in the private sector. This study extends such research to local government audits, where audit quality has been questioned. Additionally, this study investigates the effect of auditor size on perceived audit quality and satisfaction. 302 finance directors surveyed positively associated auditor expertise, responsiveness to client, professionalism, understanding of client systems, and study of internal controls with perceived audit quality. Furthermore, auditee satisfaction was positively related to auditor expertise, responsiveness to client, audit manager involvement, understanding of client systems and study of internal controls. Big 5 firms were not associated with higher levels of perceived audit quality or auditee satisfaction, despite charging significantly higher audit fees.