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    A multiple attribute decision model to compare the firms occupational health and safety management perspectives
    (Elsevier, 2017) Inan, Umut Hulusi; Gul, Sait; Yilmaz, Hafize
    Both the ferocious competition and judicial regulations of national or international authorities enforce organizations to implement a bundle of management systems (e.g. safety, quality, environmental, etc). Occupational Health and Safety Management System is a very important one because it provides guidance about the safety conditions for workplaces and health of the employees working within these areas. Measurement of a firm's OHSMS performance is vital because the firm's perspective about it is directly related with the workers' health. Besides, the comparison of the firms within an industry in terms of occupational health and safety can be informative for the authorities and the worker unions. In this study, we aim to build a multiple attribute decision making (MADM) model for determining and comparing the firms' OHSMS performances. The model utilizes the firms' OHSAS 18001:2007 implementation performances and compares them with respect to the standard's conditions. The ranking indicates each firm's OHSMS consciousness level against its competitors. We determine the importance of criteria (requirements of OHSAS) by Simos' procedure and VIKOR method ranks the firms in terms of the quality consultants' assessments. In this manner, this study introduces MADM as a possible firm comparison approach in terms of their OHSMS perspectives. (C) 2016 Elsevier Ltd. All rights reserved.
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    A multiple criteria credit rating approach utilizing social media data
    (Elsevier, 2018) Gul, Sait; Kabak, Ozgur; Topcu, Ilker
    Credit rating is a process for building a classification system for credit lenders to characterize current or potential credit borrowers. By such a process, financial institutions classify borrowers for lending decision by evaluating their financial and/or nonfinancial performances. Recently, use of social media data has emerged an important source of information. Accordingly, social media data can be very useful in evaluating companies' credibility when financial or non-financial assessments are missing or unreliable as well as when credit analyzers' subjective perceptions manipulate the decision. In this study, a multiple criteria credit rating approach is proposed to determine companies' credibility level utilizing social media data as well as financial measures. Additionally, to strengthen the lender's interpretation and inference competency, ratings are represented with a risk distribution based on cumulative belief degrees. Sentiment analysis, a web mining and text classification method, is used to collect social media data on Twitter. Importance of criteria is revealed through pairwise comparisons. Companies' performance scores and ratings are obtained by a cumulative belief degree approach. The proposed approach is applied to 64 companies. Results indicate that social media provides valuable information to determine companies' creditability. However credit ratings tend to decrease when social media data is considered.
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    An OWA Operator-Based Cumulative Belief Degrees Approach for Credit Rating
    (Wiley, 2018) Gul, Sait; Kabak, Ozgur; Topcu, Y. Ilker
    Credit lenders utilize credit rating approaches to provide a classification system for characterizing credit borrowers. In order to measure the borrowers' credibility, that is, ability and willingness to repay the debt, there are many financial and non-financial criteria that should be considered. The basic aim of this study is to propose a multiple-criteria credit rating approach that integrates different kinds of information and represents the borrowers' credibility as a distribution among all the credit ratings. The cumulative belief degree approach is proposed for this purpose. Since all the available information is used in the final representation, a distribution-based credit rating approach is expected to strengthen the lender's inference competency. In order to eliminate subjectivity in the weighting of criteria, an ordered weighted averaging operator is used. Additionally, the credit rating distribution can be transformed into a single credit rating by considering a threshold value. This study proposes a goodness-of-fit test to handle the subjectivity and difficulty of setting the threshold value. The applicability of the proposed approach is demonstrated by analyzing the credibility of selected Turkish firms from the stock exchange market of Turkey. (C) 2017 Wiley Periodicals, Inc.