Bilgisayar Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940

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  • Conference Object
    Reliability Study of Psychometric Tests in a Credit Scoring Model
    (Ieee, 2024-05-15) Nicat, Sahin; Filiz, Gozde; Ozvural, Ozden Gebizlioglu; Çakar, Tuna
    This study investigates the effectiveness and reliability of using psychometric tests in the credit decision-making processes within the finance sector. Psychometric tests, by measuring individuals' cognitive and psychological traits, hold the potential to broaden access to credit and identify high credit risk. However, after the literature review, it was seen that there was a need for more studies on the reliability and validity of these tests in finance. This study is designed to measure the test-retest reliability of a machine learning model and its inputs that utilize psychometric test results. Within the scope of the research, 115 participants were re-subjected to the same psychometric tests after an average of 6 months. Findings showed that psychometric tests and the machine learning model were generally consistent over time. This work has the potential to fill the gaps in the literature regarding the use of psychometric tests in the finance sector and lays a foundation for future research.
  • Conference Object
    Analyzing Consumer Behavior: the Impact of Retro Music in Advertisements on a Chocolate Brand and Consumer Engagement
    (IEEE, 2023-10-11) Girişken, Yener; Soyaltın, Tuğçe Ezgi; Filiz, Gözde; Çakar, Tuna; Türkyılmaz, Ceyda Aysuna
    This study presents research utilizing binary classification models to analyze consumer behaviors such as chocolate consumption and retro music ad viewing. Retro music, with its potential to evoke nostalgic feelings in consumers, is used in advertisements, which can have a significant impact on brand perception and consumer engagement. Firstly, a model focusing on chocolate consumption was developed and tested. The model yields significant outcomes. Secondly, a model based on retro music ad viewing status was developed and tested. Significant potential findings were obtained. This study emphasizes the applicability of effective classification models that can be used to understand and predict consumer behaviors, yielding significant outcomes.