Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - WoS: 55Citation - Scopus: 83Surprise Me With Your Ads! The Impacts of Guerrilla Marketing in Social Media on Brand Image(Emerald Group Publishing Ltd, 2018-11-08) Gokerik, Mehmet; Gurbuz, Ahmet; Erkan, Ismail; Mogaji, Emmanuel; Sap, SerapPurpose - The advent of social media brought a new perspective for guerrilla marketing since it allows ads to reach more people through the internet. The purpose of this paper is to investigate the influence of guerrilla marketing in social media on brand image. Design/methodology/approach - A conceptual model was developed based on the information acceptance model (IACM). The research model was validated through structural equation modelling based on the surveys of 385 university students. Findings - The results support the proposed model and confirm that guerrilla marketing in social media has a positive effect on both functional and symbolic brand image. Research limitations/implications - This study was conducted with university students. This sample was deemed appropriate since the study had to be conducted with people who use social media. However, although the age group of university students constitutes the majority of social media users, they may not fully represent the whole population. Also, this study showed four guerrilla marketing examples to participants before they commenced filling in the questionnaire. Although the authors selected the most generic guerrilla advertisements during the pilot tests and eliminated the ones which were difficult to understand, this can still be considered as limitations of the study. Practical implications - This study has both theoretical and managerial implications. First, most of the guerrilla marketing studies focus on consumers and neglect possible impacts on brands. In order to fulfil this gap in the literature, this study investigates the influence of guerrilla marketing in brand image. Besides, this study contributes to IACM by expanding its scope through testing its determinants on "brand image". It proves that IACM is valid for use in different contexts. On the managerial side, this study provides marketers with a frame of reference to understand the information adoption process of guerrilla marketing on social media. Originality/value - Current studies regarding the influence of guerrilla marketing mostly focus on consumers, where the possible impacts on brands have been relatively neglected. This study attempts to fill this gap by focussing on the brand image.Article Citation - WoS: 67Citation - Scopus: 82Social Media Utilization of Tourists for Travel-Related Purposes(Emerald Group Publishing Ltd, 2015-07-13) Oz, MustafaPurpose - The aim of this study is to determine social media use by consumers for travel-related purposes. Design/methodology/approach - A quantitative study was conducted after reviewing the related literature. The primary data were collected by means of an online questionnaire, and the results were analyzed using a statistical package program. Findings - The respondents use social media intensively both in their daily lives (> 96 per cent) and in travel-related activities (95 per cent). In addition to the detailed analyses of their usage, a model was developed to identify the factors behind social media use for travel-related purposes. Research limitations/implications - As a result of an online questionnaire method, only consumers having an Internet access could respond to the survey. Additionally, the sample was not random, and the respondents were selected only from Turkey. Consequently, the study may suffer from a generalization problem, especially for markets with major different cultural characteristics. Practical implications - The findings of the study may assist academics and practitioners to better understand social media and Web 2.0 technologies and their effects on consumers. Originality/value - One of the dominant trends affecting consumer behavior and tourism marketing in recent years is the use of social media. It is critical to understand such developments and their effects on consumer behavior that may impact on the distribution and accessibility of travel-related information.Article Citation - Scopus: 10Building a Challenging Medical Dataset for Comparative Evaluation of Classifier Capabilities(Elsevier Ltd, 2024-08) Bozkurt, Berat; Coskun, Kerem; Bakal, GokhanSince the 2000s, digitalization has been a crucial transformation in our lives. Nevertheless, digitalization brings a bulk of unstructured textual data to be processed, including articles, clinical records, web pages, and shared social media posts. As a critical analysis, the classification task classifies the given textual entities into correct categories. Categorizing documents from different domains is straightforward since the instances are unlikely to contain similar contexts. However, document classification in a single domain is more complicated due to sharing the same context. Thus, we aim to classify medical articles about four common cancer types (Leukemia, Non-Hodgkin Lymphoma, Bladder Cancer, and Thyroid Cancer) by constructing machine learning and deep learning models. We used 383,914 medical articles about four common cancer types collected by the PubMed API. To build classification models, we split the dataset into 70% as training, 20% as testing, and 10% as validation. We built widely used machine-learning (Logistic Regression, XGBoost, CatBoost, and Random Forest Classifiers) and modern deep-learning (convolutional neural networks - CNN, long short-term memory - LSTM, and gated recurrent unit - GRU) models. We computed the average classification performances (precision, recall, F-score) to evaluate the models over ten distinct dataset splits. The best-performing deep learning model(s) yielded a superior F1 score of 98%. However, traditional machine learning models also achieved reasonably high F1 scores, 95% for the worst-performing case. Ultimately, we constructed multiple models to classify articles, which compose a hard-to-classify dataset in the medical domain. © 2024 Elsevier B.V., All rights reserved.