Reklam filmlerinde dış ses etkisinin optik beyin görüntüleme ve derinlemesine mülakat yöntemleriyle saptanması

Tuna Çakar (MEF Author)Voice-overs are used extensively to increase the effectiveness of the TV ads especially in the last decade. The main purpose is to provide the brand message via a clear feature that will inevitably grab the attention of the viewers. The current study contains the neuro tests of 12 TV ads in banking and finance sectors on 168 participants in 8 groups. Optic brain imaging (fNIRS) and in-depth interviews were the methodologies utilized during the test of these TV ads. The obtained results indicate that the use of voice-over during the TV ads possibly causes the decrease in attention and emotional engagement levels of the participants.WOS:000410450200003Emerging Sources Citation IndexArticleEkim2016YÖK - 2016-1

Unraveling neural pathways of political engagement: bridging neuromarketing and political science for understanding voter behavior and political leader perception

Political neuromarketing is an interdisciplinary field that combines marketing, neuroscience, and psychology to understand voter behavior and political leader perception. This interdisciplinary field offers novel techniques to understand complex phenomena such as voter engagement, political leadership, and party branding. This study aims to understand the neural activation patterns of voters when they are exposed to political leaders using functional near-infrared spectroscopy (fNIRS) and machine learning methods. We recruited participants and recorded their brain activity using fNIRS when they were exposed to images of different political leaders. This neuroimaging method (fNIRS) reveals brain regions central to brand perception, including the dorsolateral prefrontal cortex (dlPFC), the dorsomedial prefrontal cortex (dmPFC), and the ventromedial prefrontal cortex (vmPFC). Machine learning methods were used to predict the participants' perceptions of leaders based on their brain activity. The study has identified the brain regions that are involved in processing political stimuli and making judgments about political leaders. Within this study, the best-performing machine learning model, LightGBM, achieved a highest accuracy score of 0.78, underscoring its efficacy in predicting voters' perceptions of political leaders based on the brain activity of the former. The findings from this study provide new insights into the neural basis of political decision-making and the development of effective political marketing campaigns while bridging neuromarketing, political science and machine learning, in turn enabling predictive insights into voter preferences and behaviorWOS:0011358179000012-s2.0-8518158251138188505Science Citation Index ExpandedArticleUluslararası işbirliği ile yapılmayan - HAYIROcak2024YÖK - 2023-24Kası

What Is Neuroentrepreneurship? The Theoretical Framework, Critical Evaluation, And Research Program

As interest in entrepreneurship research to identify the possible economic development opportunities that entrepreneurs can provide, entrepreneurship research's objective tools are reaching their limits. Researchers in entrepreneurship are striving to discover new techniques and methodologies to answer questions about what makes a person an entrepreneur and perhaps identify and encourage an entrepreneur in the next step. Although a great deal of research has been done to answer these questions scientifically, traditional techniques have failed to produce the desired answers. For this reason, researchers working in the entrepreneurship field have recently been increasingly interested in applying neuroscience methods, especially after the proliferation of research fields such as neuroeconomics, neuromarketing, and neuropolitics. Although the population of neuroentrepreneurship research is gradually increasing, we cannot say that the field has been studied theoretically enough yet. In this article, a theoretical definition of neuroentrepreneurship is made, and a scientific framework is tried to be gained on the way to future research.52126

An exploratory study on the effect of contour types on decision making via optic brain imaging method (fNIRS)

Decision-making is a combination of our positive anticipations from the future with the contribution of our past experiences, emotions, and what we perceive at the moment. Therefore, the cues perceived from the environment play an important role in shaping the decisions. Contours, which are the hidden identity of the objects, are among these cues. Aesthetic evaluation, on the other hand, has been shown to have a profound impact on decision-making, both as a subjective experience of beauty and as having an evolutionary background. The aim of this empirical study is to explain the effect of contour types on preference decisions in the prefrontal cortex through risk-taking and aesthetic appraisal. The obtained findings indicated a relation between preference decision, contour type, and PFC subregion. The results of the current study suggest that contour type is an effective cue in decision-making, furthermore, left OFC and right dlPFC respond differently to contour types

Warning notes in a learner’s dictionary: A study of the effectiveness of different formats

This study used an online correction task to explore the extent to which different types of warning notes in Longman Dictionary of Contemporary English Online were heeded when users tried to correct errors in the use of L2 target words. The task was completed by 332 participants, yielding 1,819 answers produced after clicking on links to relevant entries. Warning notes were categorised in terms of their formatting features, but there were found to be inconsistencies in the way the dictionary associated different categories with different kinds of learner error. Participants judged warning notes with more visual enhancements to be more useful, but in the correction task the position of the warning notes also seemed to affect the degree to which the warnings were successfully applied. Different types of warning notes in learners’ dictionaries have not been examined previously in any depth, and the results suggest that some adjustments to formatting and placement might make them more effective.Oca

Performing DISC Personal inventory analysis in job postings using artificial intelligence methods

One of the application fields of DISC selfevaluation analysis was introduced to predict people's performance and orientation in their working life. Each letter in the word DISC represents an essential personal characteristic, dividing the profiles of people in business life into four essential parts. In the current study, DISC analysis is conducted on job postings to match the person with the job posting. The current study was based on the analysis of 3 different datasets with job postings in English, Turkish and Romanian prepared by using web scraping methods and then labeled in accordance with DISC criteria. Several different machine learning algorithms have been performed on the DISC analysis outputs, and they reached the best results with accuracy values of around over 96% on the English dataset, around over 95% on the Turkish dataset, and around over 96% on the Romanian dataset, for both D, I, S, C models.Aralı

Customer segmentation and churn prediction via customer metrics

Bu çalışmada faktoring sektöründe faaliyet gösteren müşterilerin geçmişte yapmış oldukları işlem hareketleri ve sahip oldukları risk, limit ve şirket verileri üzerinden, son işlem tarihlerinden sonra gelecek üç ay içerisinde işlem yapmaya devam edip etmemelerini veri güdümlü makine öğrenimi modelleri kullanarak tahmin edilmesi amaçlandı. Kurulan modeller sonucunda iki farklı müşteri grubunun (Gerçek ve Tüzel şirket) Kayıp Analizi (Churn) gerçekleştirildi. XGBoost modeli ile %74 ve %77 oranında F1-Skoru ile tahmin edildi. Bu modelleme sayesinde ayrılacak olan müşterilerin tahminlemesi ile birlikte bu müşteri gruplarına yapılacak özel promosyonlar, kampanyalar sayesinde müşterileri elde tutma oranının artırılması amaçlandı. Elde tutma oranlarının artması sayesinde şirket bazında işlem hacmine doğrudan katkı yapılması sağlandı.In this study, it is aimed to predict whether customers operating in the factoring sector will continue to trade in the next three months after the last transaction date, using data- driven machine learning models, based on their past transaction movements and their risk, limit and company data. As a result of the models established, Loss Analysis (Churn) of two different customer groups (Real and Legal factory) wascarried out. It was estimated by the XGBoost model with anF1 Score of 74% and 77%. Thanks to this modeling, it was aimed to increase the retention rate of customers through special promotions and campaigns to be made to these customer groups, together with the prediction of the customerswho will leave. Thanks to the increase in retention rates, a direct contribution to the transaction volume on a company basis was ensured.Scopus - Affiliation ID: 60105072Mayı

Satın alma davranışının beyin ön bölgesindeki izdüşümlerinin fnirs yöntemiyle incelenmesi

TÜBİTAK EEEAG15.10.201

An Exploratory Study on the Effect of Contour Types on Decision Making via Optic Brain Imaging Method (fNIRS)

Decision-making is a combination of our positive anticipations from the future with the contribution of our past experiences, emotions, and what we perceive at the moment. Therefore, the cues perceived from the environment play an important role in shaping the decisions. Contours, which are the hidden identity of the objects, are among these cues. Aesthetic evaluation, on the other hand, has been shown to have a profound impact on decision-making, both as a subjective experience of beauty and as having an evolutionary background. The aim of this empirical study is to explain the effect of contour types on preference decisions in the prefrontal cortex through risk-taking and aesthetic appraisal. The obtained findings indicated a relation between preference decision, contour type, and PFC subregion. The results of the current study suggest that contour type is an effective cue in decision-making, furthermore, left OFC and right dlPFC respond differently to contour types

The Neural Correlates of the Effect of Belief in Free Will on Third-Party Punishment: An Optical Brain Imaging (fNIRS) Study

Third party punishment (TPP), or altruistic punishment, is specifically human prosocial behavior. TPP denotes the administration of a sanction to a transgressor by an individual that is not affected by the transgression. In some evolutionary accounts, TPP is considered crucial for the stability of cooperation and solidarity in larger groups formed by genetically unrelated individuals. Belief in free will (BFW), on the other hand, is the idea that humans have control over their behavior. BFW is a human universal notion that, in some studies, has been found to be supportive of prosocial behavior. In our study, we examined the effect of BFW on TPP under high and low affect scenarios through optical brain imaging (fNIRS). We hypothesized that in low affect cases, there would be a positive correlation between the strength of the BFW and the severity of the punishment inflicted. Obtained results and related statistical analyses indicate that participants with higher degree of BFW have more neural activation in their right dorsolateral prefrontal cortex (DLPFC) (hbo and hbt measures) in high affect scenarios, whereas the participants with lower degree of BFW have higher levels of neural activation in the medial PFC (hbo and hbt measures) in low affect scenarios. These empirical findings are in line with the research findings in the relevant academic literature and support the hypothesis that the degree of BFW influences punishment decisions