İşletme Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1937

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Department: search.filters.department.İİSBF, İşletme BölümüSubject: search.filters.subject.Machine learning

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  • Review
    Citation - WoS: 8
    Citation - Scopus: 10
    Consumer Responses Toward Smart Technology: a Systematic Review, Synthesis, and Future Research Agenda
    (Wiley, 2024-04-25) Köse, Şirin Gizem; Özer Çizer, Ece; Sağkaya Güngör, Ayşegül; Ozansoy Çadırcı, Tugce; Cadirci, Tugce Ozansoy; Gungor, Aysegul Sagkaya
    This article is a comprehensive review of the literature on smart technology in consumer studies from 1996 to 2023. While the paper provides information about the development of the field by identifying important publications and authors, it employs topic modeling to pinpoint key topics in papers published in marketing and business journals. These topics are then grouped into three research streams and evaluated concerning theoretical, contextual, and methodological perspectives. While doing so, specific gaps were identified. By revealing gaps in the literature, the study suggests promising avenues for further research. Finally, this article advances our comprehension of the smart technology literature in marketing and business journals and informs future inquiry in this rapidly evolving domain.
  • Conference Object
    Prediction of Loan Decisions With Optical Neuroimaging (fnirs) and Machine Learning
    (IEEE, 2023-07-05) Girişken, Yener; Son Turan, Semen; Çakar, Tuna; Ertuğrul, Seyit; Sayar, Alperen; Son, Semen; Giriken, Yener
    The successful applications of neuroscientific methods and artificial learning approaches have increased in applied fields such as economics, marketing, and finance in the last decade. In this study, a prediction model was developed using the output of optical neuroimaging (fNIRS) measurements from the prefrontal brain regions while 40 participants made decisions for 35 credit offers. The aim was to predict participants' responses to credit offers using artificial learning methods based on four metrics obtained over time from the optical neuroimaging system. The findings of the study indicate that the first 6 seconds (prior to the response entry) are particularly critical. While the performance rate in the developed prediction models is found to be higher, especially in tree-based algorithms, this paper includes a performance comparison of 5 models specifically.