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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.

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.

Previous work highlights two key challenges in searching for information about individual entities (such as persons, places and organisations) over semantic data: query ambiguity and redundant attributes. The purpose of this paper is to consider these challenges and proposes the Attribute Importance Model (AIM) for clustering and ranking aggregated entity search to improve the overall users’ experience of finding and navigating entities over the Web of Data.

The proposed model describes three distinct techniques for augmenting semantic search: first, presenting entity type-based query suggestions; second, clustering aggregated attributes; and third, ranking attributes based on their importance to a given query. To evaluate the model, 36 subjects were recruited to experience entity search with and without AIM.

The experimental results show that the model achieves significant improvements over the default method of semantic aggregated search provided by Sig.ma, a leading entity search and navigation tool.

This proposal develops more informative views for aggregated entity search and exploration to enhance users’ understanding of semantic data. The user study is the first to evaluate user interaction with Sig.ma's search capabilities in a systematic way.

This paper investigates the impact of earnings management on value relevance of accounting information in the context of Japan. Researchers carrying out earnings management research usually rely on the Jones (1991) or the modified Jones model (1995) to disaggregate accruals into its discretionary and non‐discretionary components. However, because of criticisms leveled against extant models of discretionary accruals, this study instead uses earnings management measures constructed by Leuz et al. (2001) and Bhattacharya et al. (2001) and examines the relationship between these measures and their impact on the value‐relevance of accounting information. The latter is operationalized by the explanatory power of book values and earnings (combined model) and earnings alone (earnings model) for stock price. Results based on 5,318 consolidated firm‐year observations over 1992‐1999 show that, both earnings management measures and aggregate earnings management measures (combination of both earnings smoothing and earnings management measures) are significantly negatively associated with the combined value relevance of book values of equity and earnings (combined model) and value relevance of earnings (earnings model).

There have been numerous efforts to tackle the problem of accumulated construction and demolition wastes worldwide. In this regard, this study develops a model for identifying the optimum fleet required for waste transportation. The proposed model is validated through a case study from the construction sector in New Cairo, Egypt.

Various fleet combinations are assessed against the time, cost, energy and emissions generated from waste transportation. Genetic algorithm optimization is performed to select the near-optimum solutions. Complex proportional assessment and operational competitiveness rating analysis decision-making techniques are applied to rank Pareto frontier solutions. These rankings are aggregated using an ensemble approach based on the half-quadratic theory. Finally, a sensitivity analysis is implemented to determine the most sensitive attribute.

The results reveal that the optimum fleet required for construction and demolition wastes (CDW) transportation consists of one wheel loader of bucket capacity 2.5 cubic meters and nine trucks of capacity 22 cubic meters. Furthermore, consensus index and trust level of 0.999 are obtained for the final ranking. This indicates that there is a high level of agreement between the rankings. Moreover, the most sensitive criterion (i.e. energy) is identified using a sensitivity analysis.

This study proposes an efficient and effective construction and demolition waste transportation strategy that will lead to economic gains and protect the environment. It aims to select the optimum fleet required for waste transportation based on economic, social and environmental aspects. The usefulness of this study is establishing a consensual decision through the aggregation of conflicting decision makers' preferences in waste transportation and management.

The purpose of this paper is to report a research on application of ORESTE (Organization, Rangement Et Synthese De Donnees Relationnelles, when translated into English means organization, storage and summarizing data relational) for agile concept selection.

The concept selection problem has been formulated as a multi-criteria decision-making (MCDM) problem to select the best concept design of the rotary switch component. ORESTE has been used as solution methodology. The selected concept design alternatives were subjected to implementation.

As ORESTE is a preferential outranking method, it uses ordinal data, enabling decision makers in situations where practically sufficient data are not available. This paper uses ORESTE for agile concept selection where gathering sufficient data is difficult. Also, the results generated by ORESTE have been compared with technique for order performance by similarity to an ideal solution.

In the present study, ORESTE has been applied for concept selection of rotary switch component. In future, more number of studies need to be conducted to select more concept design alternatives where getting prior information is difficult or consumes more time to ensure practical effectiveness of ORESTE.

The application of ORESTE for concept design selection enabled the practitioners to select the best concept with ordinal data and also enabled scope for indifferentiability and incomparableness of any two alternatives in the final outranking.

Formulation of MCDM problem for agile concept selection and solution generation using ORESTE is the original contribution of the authors.

The purpose of this paper is to propose a practical framework to measure the safety performance of workers in the Indian construction industry. The key safety performance indicators are identified and ordered on the premise that the higher order assignment of an indicator implies a strong indication of an effective safety performance.

Various indicators of safety performance in the construction industry were identified from extant literature review combined with author's personal viewpoint. The identified variables were inquired for appropriateness for the Indian construction scenario by consultation with experts. Fuzzy Technique for order preference by similarity to ideal solution (TOPSIS) technique was considered for the ranking of the indicators from most to least important.

The most important highlight of the study was the importance of the role of management by participating in informing workers about the safety rules and compliance toward safety measures. Proper and timely safety training to the workers and equipping them with sophisticated safety equipment for daily activities is perceived to be highly important in ensuring a safe and healthy workplace environment. Controlling the absenteeism rate reduces the burden of extra work on the employees, thereby, encouraging safe work-related behavior.

Senior management should make safety induction programs compulsory at the time of joining of the employees. The guidelines for safety practices, rules and information about the safety equipment should be properly documented and arranged in safety manuals. Periodical drills involving visual demonstration of the safety practices should be followed to ensure safety at workplace.

The purpose of this paper is to examine the institutional factors that affect the productivity of individuals in the field of operations.

This study identifies a sample of graduates from PhD programs in operations and utilizes a partial least squares analysis to examine the effect of academic origin, academic affiliation, and advisor productivity on each individual's research productivity.

The results of the analysis indicate that the productivity of an individual is directly influenced by the aggregate prestige of the institutions where the individual was employed during article publication and indirectly influenced by the prestige of the institution where the individual received the terminal degree. Additionally, differences were found between groups when the sample was divided by focus (operations management (OM) versus operations research (OR). The OM model held the same relationships as the combined model, while the OR model included significant direct effects of academic origin and indirect effects of the advisor's productivity on the individual's productivity.

This research is the first to fully evaluate the institutional antecedents to research productivity of individuals in operations. In doing so, valuable insights are gained as to how to facilitate the success of researchers in operations. Additionally, factors are highlighted that should be considered by institutions looking at hiring freshly minted PhDs. Finally, these results can benefit practitioners when considering working with academics as a source of emerging information or consulting.

Based on an efficiency analysis of the Global Entrepreneurship Index (GEI), the purpose was to demonstrate that the Key Performance Indicators’ analysis leads to a misinterpretation of the dynamics of National Systems of Entrepreneurship (NSEs). This might hamper the formulation of sound initiatives in other economies, with relevant implications for developing countries.

This study categorized GEI indicators into output and input indicators. Following this procedure, each dimension was analyzed separately and then compared to each other, considering countries’ productivity rates. The main focus is given to the case of the US, the usual benchmark for NSEs and leader in the GEI Index. Lastly, a taxonomy of NSEs according to their efficiency levels was developed.

The findings of the analysis demonstrates that innovation-driven economies with lower positions in GEI ranking often have higher productivity rates when compared to economies with higher positions in GEI ranking. Specifically, the US appears not to be a good benchmark in terms of NSE efficiency.

The study’s approach is limited in scope by data availability on NSEs and the use of GEI, a representation of aggregate patterns of country-level entrepreneurial dynamics. More refined data are needed in order to clarify some insights from this research.

The perception of systemic efficiency should be considered more thoroughly when designing dedicated entrepreneurship-oriented policies in other countries that aim at establishing a more vibrant entrepreneurial system while facing resource constraints.

Simplistic views of systemic aspects may hamper the formulation of sound entrepreneurship-oriented initiatives with particularly relevant implications for public policy in laggard economies.

The value of this article relies on applied a simple metric – efficiency ratio – order than, e.g. data envelopment analysis to portray a key issue related to the interpretation of supranational rankings related to the entrepreneurship ecosystem make mainly by policymakers and scholars that is: pick the 1st one, follow the leader.

Science and technology parks (STPs) have a limited capacity, which can create challenging conditions for applicants. This makes the location selection a multi-criteria decision-making (MCDM) problem to find and apply for the most appropriate STP with the highest accordance with the startup's requirements. This research aims to select the most appropriate STP to locate an international entrepreneurial pharmaceutical startup under uncertainty. Since drugs are generally produced domestically in developing countries such as Iran, the access of pharmaceutical startups to the resources provided by STPs can lead to overcoming competitors and improving the country's health system.

In this research, the factors or attributes effective on startup location were extracted through a two-round Delphi method, which was performed among 15 experts within three groups. Subsequently, the determining factors were used to select the location of a pharmaceutical startup among possible STPs. In this regard, decision-makers were allowed to use different types of numbers to transfer their opinion. Afterward, the heterogeneous weighted aggregated sum product assessment (HWASPAS) method was applied to calculate the score of each alternative and rank them to place the studied startup successfully.

The results indicated that Tehran STP stands in the first place; however, if the decision was made based on single criterion like cost, some other STPs could be preferable, and many managers would lose this choice. Furthermore, the results of the proposed method were close to other popular heterogeneous MCDM approaches.

A heterogeneous WASPAS is developed in this article for the first time to enable international entrepreneurs to imply their opinion with various values and linguistic variables to reduce the emphasis on accurate data in an uncertain environment.