Ticari Bilimler Fakültesi / Faculty of Commercial Science

Permanent URI for this collectionhttps://hdl.handle.net/11727/2074

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Start date: 2020End date: 2024

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    Use Of Advanced Measurement And Reality Technologies In Cultural Heritage Sites From The Perspective Of Technology And Tourism
    (Başkent Üniversitesi Ticari Bilimler Fakültesi, 2024-03-12) Varol, Fatih; Oksuz, Merve
    In recent years, in parallel with technological innovations, the use of modern techniques instead of traditional methods has become widespread in studies on archiving, preserving, restoring and visiting cultural heritage (CH) in the digital environment. Reality technologies, advanced measurement methods in engineering and mobile applications have brought a multidisciplinary perspective to the subject of sustainability of CH sites and visiting them within the scope of tourism. This study investigates the contribution of studies carried out with the latest technological opportunities in CH areas to sustainability and tourism activities. It reveals with the most concrete examples how the fastest LIDAR (Light Detection and Ranging) technology used in remote sensing of concrete objects, as well as close range photogrammetry and unmanned aerial vehicle (UAV) photogrammetry applications can be integrated into the sustainability of CH and reality technologies. The three-dimensional (3D) images obtained in this way have enabled world-famous ancient cities, museums, natural and archaeological sites to be visited virtually with the help of augmented reality (AR), virtual reality (VR) and mixed reality (MR) applications. These methods also provide significant advantages in terms of time and cost compared to traditional technologies in the digital documentation of CH and VR studies.
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    Reduced-Order Modeling For Heston Stochastic Volatility Model
    (HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS, 2024) Kozpinar, Sinem; Uzunca, Murat; Karasozen, Bulent
    In this paper, we compare the intrusive proper orthogonal decomposition (POD) with Galerkin projection and the data-driven dynamic mode decomposition (DMD), for Heston's option pricing model. The full order model is obtained by discontinuous Galerkin discretization in space and backward Euler in time. Numerical results for butterfly spread, European and digital call options reveal that in general DMD requires more modes than the POD modes for the same level of accuracy. However, the speed-up factors are much higher for DMD than POD due to the non-intrusive nature of the DMD.
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    Factors Determining Consumer Concerns About Clothing Industry Problems
    (2023) Erol boyaci, Gulay; Senturk ozer, Leyla; HOC-6390-2023
    Although the public concern towards unsustainable current production and consumption is growing, the inadequacy of global regulations on sustainability issues puts the responsibility on consumers. However, despite their concerns, some consumers need help shopping from sustainable brands. The current research examines some antecedents of consumer concern towards the clothing industry's environmental and sweatshop issues. Data is obtained from 372 Turkish fast fashion consumers through online platforms. Level of exposure to informative posts on social media platforms, the perceived knowledge and belief towards the issues positively affect concern. Also, decreasing concern towards these problems with advancing age is observed in Turkiye.
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    Women Board Members' Impact on ESG Disclosure with Environment and Social Dimensions: Evidence from The European Banking Sector
    (2023) Gurol, Burcu; Lagasio, Valentina; https://orcid.org/0000-0001-9974-2351; HGC-2474-2022
    PurposeThis study aims to investigate the relationship between banks' board structure and sustainability performance. Design/methodology/approachThe empirical quantitative paper covers a sample of 35 European banks that are listed at the EUROSTOXX 600. Regression analysis techniques were used in the analyses. FindingsResults indicate that board size, women ratio and independent directors ratio on board are positively and significantly related to environmental social governance (ESG), E and S disclosure scores. Also, we find that ESG disclosure is related to bank profitability. Practical implicationsFindings have implications for both policymakers and practitioners (bankers and investors). Large bank boards, which have women and independent members, could perform better in terms of ESG disclosure. The results also show that large banks and banks with high borrowing care more about sustainability. For banks to reach resources, they should perform well in terms of sustainability disclosure to their stakeholders. Social implicationsBanks should observe academic findings on corporate governance (CG) practices, which lead to a better ESG disclosure to structure their CG to improve at the best their disclosure policies: they should prefer larger boards with a high level of women and independence. In addition, we attach importance to the ESG performance of the banking sector due to its fund transfer functions. Banks transfer the deposits they collect to those in need of funds as loans. For this reason, it is important to which sector and which business they give credit. The importance of banks on ESG and their adoption of sustainability dimensions also affect their credit decisions. Originality/valueThis study examines the relationship between banks' board structure variables and their effect on ESG, E and S scores separately. This study thinks that the G score can be a handicap for ESG-CG relations. Because chosen CG variables (women ratio, independent ratio, board size) affect G scores positively and can reason for positive ESG-CG relation. The environmental and social impact of women ratio, independent ratio and board size can be seen in this study.
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    The Intellectual Structure of the Information Systems Field: Research Styles and Publication Patterns of North American and European Paradigms
    (2022) Ozkoc, Esma Erguner; Kefkir, Teoman Ahmet; Kirkbesoglu, Erdem; https://orcid.org/0000-0002-6781-9753; AAG-1506-2021; ABI-3973-2020
    This paper identifies the researches that have had the greatest impact on the Information Systems (IS) discipline and analyses the changes that have taken place in the intellectual structure of this discipline within the ongoing paradigmatic debates between Europe and North America. The methodology applies citation analysis and social network analysis to the articles published in four European and North American journals with the highest impact factors in the IS field. The findings of the study reveal a significant difference between the research styles and publication patterns of European and North American research traditions.
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    Effect of Polynomial, Radial Basis, and Pearson VII Function Kernels in Support Vector Machine Algorithm for Classification of Crayfish
    (2022) Garabaghi, Farid Hassanbaki; Benzer, Recep; Benzer, Semra; Gunal, Aysel Caglan; 0000-0002-5339-0554; A-5050-2014
    Freshwater crayfish are one of the most important aquatic organisms that play a pivotal role in the aquatic food chain as well as serving as bioindicators for the aquatic ecosystem health assessment. Hemocytes, the blood cells of crustaceans, can be considered stress and health indicators in crayfish, and are used to evaluate the health response. Therefore, total hemocyte cell numbers (THCs) are useful parameters to show the health of crustaceans and serve as stress indicators to decide the quality of the habitat. Since, catching the fish and the other aquatic organisms, and collecting the data for further assessments are time-consuming and frustrating, today, scientists tend to use swift, more sophisticated, and more reliable methods for modeling the ecosystem stressors based on bioindicators. One tool which has attracted the attention of science communities in the last decades is machine learning algorithms that are reliable and accurate methods to solve classification and regression problems. In this study, a support vector machine is carried out as a machine learning algorithm to classify healthy and unhealthy crayfish based on physiological characteristics. To solve the non-linearity problem of the data by transporting data to high-dimensional space, different kernel functions including polynomial (PK), Pearson VII function-based universal (PUK), and radial basis function (RBF) kernels are used and their effect on the performance of the SVM model was evaluated. Both PK and PUK functions performed well in classifying the crayfish. RBF, however, had an adverse impact on the performance of the model. PUK kernel exhibited an outstanding performance (Accu-racy = 100%) for the classification of the healthy and unhealthy crayfish.
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    Debt maturity structure and stock price crash risk: The case of Turkiye
    (2022) Canbaloglu, Bilge; Alp, Ozge Sezgin; Gurgun, Gozde; AAG-3428-2021
    This study explores the relationship between the debt maturity structure and the stock price crash risk for nonfinancial firms on the Borsa Istanbul from 2009 to 2019. Family ownership is added to the analyses to provide a new perspective on the literature examining the link between stock price crash risk and debt structure. The results show that an increase in long-term debt reduces the risk of a stock price crash, as efficient corporate management practices not only result in favorable debt issuance at a longer term but also reduces information asymmetry that leads to crash risk at firms. Furthermore, the mitigating effect of long-term debt on crash risk is more pronounced at family-controlled firms because active monitoring by family members causes lower agency conflicts in obtaining longer-term debt as well as in maintaining family status, which in turn also diminishes future stock price crashes. Copyright (c) 2022 Borsa Istanbul Anonim S, irketi. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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    Machine learning-enabled healthcare information systems in view of Industrial Information Integration Engineering
    (2022) Uysal, Murat Pasa; 0000-0002-8349-9403
    Recent studies on Machine learning (ML) and its industrial applications report that ML-enabled systems may be at a high risk of failure or they can easily fall short of business objectives. Cutting-edge developments in this field have increased complexity and also brought new challenges for enterprise information integration. This situation can even get worse when considering the vital importance of ML-enabled healthcare information systems (HEIS). Therefore, the main argument of this paper is that we need to adopt the principles of Industrial Information Integration Engineering (IIIE) for the design, development, and deployment processes of ML-enabled systems. A mixed research paradigm is adopted, and therefore, this study is conducted by following the guidelines and principles of Action Research, Design Science Research, and IIIE. The contributions of this study are two-fold: (a) to draw researchers' and practitioners' attention to the integration problems of ML-enabled systems and discuss them in view of IIIE, and (b) to propose an enterprise integration architecture for ML-enabled HEIS of a uni-versity hospital, which is designed and developed by following the guidelines and principles of IIIE.
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    The efficiency of primary sovereign bond markets in Turkey: The so-called Fisher puzzle reconsidered
    (2022) Sunal, Onur; 0000-0002-3972-4060
    Many prior studies have tested the validity of the Fisher effect, with results proving controversial, regardless of the econometric models, country groups, or time spans chosen. Therefore, to solve the so-called Fisher puzzle, this study aims to reveal whether current interest rates, which are set in the primary bond markets, carry information about actual inflation rates when the conventional direction of causation is reversed, using monthly Turkish data from the 2010-2018 period. In line with our expectations, we found a significant long-run coefficient (0.92), which indicates that changes in interest rates are rational expectations of changes in current inflation rates, though a full Fisher effect was not observed. Moreover, the short-run coefficients were also significant, which highlights the fact that the unanticipated movements in these variables act as early signals of persistent future price-level changes. Therefore, monetary authorities should respond rapidly in the short run using rules-based proactive policies to curb long-run volatilities, which also restrict the power of estimations, as market participants tend to assign higher risk premiums to bond yields when prices are expected to surge. (C) 2021 Economic Society of Australia, Queensland. Published by Elsevier B.V. All rights reserved.
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    Morphometric analysis of Crayfish - traditional and artificial intelligent approach
    (2022) Benzer, Semra; Benzer, Recep; 0000-0002-5339-0554; A-5050-2014
    Crayfish are crustaceans of cultural importance in most countries, traditionally consumed on important occasions for centuries, and of high economic value in the market. This study was carried out to analyze some morphological characteristics, the total length of total weight, the carapace length total length, the chela length total length, the abdomen length total length relationships, properties, and ratios of freshwater crayfish (Astacus leptodactylus Eschscholtz 1823) in Iznik Lake. Length-weight and length-length relationships with traditional methods and artificial neural networks, which are the most important subfields of artificial intelligence, have been evaluated. The total length-weight relationships for males, females and all individuals were found to be: W = 0.08197221 L (2.61) (R-2 = 0.941), W = 0.08252047 L (2.53) (R-2 = 0.948) and W = 0.06874014 L (2.65) (R-2 = 0.927), respectively. As a result, the morphometric relationships in Astacus leptodactylus examined in this study will provide information for future studies and monitoring management plans with traditional and artificial intelligence approaches.