Browsing by Author "Kahraman, A"

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    A Qualitative Research of Young People's Motivation to Start, Continue, Reduce and Quit Playing Online Multiplayer Games on Computer
    Kahraman, A; Kazançoglu, I
    The aim of this research is to understand why young people start, continue, reduce, and intend to quit playing online multiplayer games. In-depth interviews were conducted with 25 male undergraduate students who continue to play online multiplayer games. Interview transcripts were analyzed through MAXQDA 2020 with content analysis. The four themes and eleven categories were revealed: starting (social, involvement), continuing (achievement, social, immersion, enjoyment, monetary), reducing (conflict, negative emotions) and intention to quit (non-involvement, self-regulation). The most-reported categories under each theme were involvement, achievement, conflict, and non-involvement, respectively. Socializing was the most-reported subcategory for the starting theme; advancement, refreshment, socializing for the continuing theme; deterioration of performance and health for the reducing theme; lack of interest/enjoyment; lack of time for intention to quit theme. The study contributes by providing a holistic perspective for understanding young peoples' motivation factors to start, continue, reduce, and intend to quit.
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    Understanding consumers' purchase intentions toward natural-claimed products: A qualitative research in personal care products
    Kahraman, A; Kazancoglu, I
    In recent years there is a trend of consuming natural products for a sustainable and healthier life. Therefore, firms began aligning their strategy with sustainability by communication strategies that they produce natural products, which are better for health as well as the environmental sustainability. However, sometimes these claims may be deceptive. The purpose of this paper is to understand the consumers' purchasing intentions toward products claiming naturalness in their advertising and packaging strategies. This research also examined greenwashing perceptions and their potential roles in purchasing intentions. In-depth face-to-face interviews carried out with 20 Turkish women regarding personal care products (local brand and international brand). The findings of the interviews revealed eight themes (perceived greenwashing, perceived green image, price perception, environmental concern, green trust, skepticism, perceived risk, and purchase intention). This study contributes to predict a framework from consumer viewpoint for identifying the themes related to greenwashing.
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    A conceptual framework for barriers of circular supply chains for sustainability in the textile industry
    Kazancoglu, I; Kazancoglu, Y; Yarimoglu, E; Kahraman, A
    Circular economy is a contemporary concept including usage of renewable materials and technologies. The transition to the circular economy creates value through closed-loop systems, reverse logistics, eco-design, product life cycle management, and clean production. The aim of the study was to propose a holistic conceptual framework for barriers of circular supply chain for sustainability in the textile industry. Within this aim, an in-depth literature review on barriers was conducted by covering all supply chain stages and circular initiatives in textile industry. Then, a focus group study was implemented. In the focus group study, barriers related to supply chains that prevent companies to implement the circular economy were discussed and validated. As a result, a total of 25 barriers were classified under nine main categories such as (a) management and decision-making, (b) labour, (c) design challenges, (d) materials, (e) rules and regulations, (f) lack of knowledge and awareness, (g) lack of integration and collaboration, (h) cost, and (i) technical infrastructure.
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    How does training given to pediatric nurses about artificial intelligence and robot nurses affect their opinions and attitude levels? A quasi-experimental study
    Karaarslan, D; Kahraman, A; Ergin, E
    Purpose: This study was conducted to investigate the effect of training provided to pediatric nurses on their knowledge and attitude levels about artificial intelligence and robot nurses. Design and methods: In this study, a single -group pre- and post-test quasi -experimental design was used. Data were collected from pediatric nurses working in Training and Research Hospital located in western Turkey. Forty-three pediatric nurses participated in the study. The study data were collected using the Pediatric Nurses' Descriptive Characteristics Form , Artificial Intelligence Knowledge Form , and Artificial Intelligence General Attitude Scale . Results: The mean scores of the participating pediatric nurses obtained from the Artificial Intelligence Knowledge Form before, right after and one month after the training were 41.16 +/- 14.95, 68.25 +/- 13.57 and 69.06 +/- 13.19, respectively. The mean scores they obtained from the Positive Attitudes towards Artificial Intelligence subscale of the Artificial Intelligence General Attitude Scale before and after the training were 3.43 +/- 0.54 and 3.59 +/- 0.60, respectively whereas the mean scores they obtained from its Negative Attitudes towards Artificial Intelligence subscale were 2.68 +/- 0.67 and 2.77 +/- 0.75, respectively. Conclusions: It was determined that the training given to the pediatric nurses about artificial intelligence and robot nurses increased the nurses' knowledge levels and their artificial intelligence attitude scores, but this increase in the artificial intelligence attitude scores was not significant. Practice implications: The use of artificial intelligence and robotics or advanced technology in pediatric nursing care can be fostered. (c) 2024 Elsevier Inc. All rights reserved.
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    The Effect Of Corporate Social Responsibility During Product-Harm Crisis: Proposing A Model Related To Consumers' Attribution Process
    Kahraman, A
    The rapid transformations in environmental factors make firms to expose to crisis. Preventing the emergence of the crisis is not always possible, however successful crisis management can be minimize the negative effects. Acquittal campaigns which emphasize the positive image of the firm before crisis may be used frequently during crisis management period. In such campaigns social responsibility activities which is one of the dimensions of reputation is drawn dramatically. The aim of this research is to reveal the effect of being a socially responsible company on the elements of consumers' attribution process. In this regard, a model that addresses the attribution process is proposed and to test this model a quantitative research is designed based on the scenarios, then responses of the 1000 participants are evaluated. The results show that attribution process occurs in the form as attribution-emotion-behavior like in the proposed model, the effects of social responsibility on the components of attribution process change based on being socially responsible or not.
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    The role of the city in the innovative software entrepreneurship ecosystem: a qualitative study
    Özdogan, B; Dirik, D; Kahraman, A
    While software entrepreneurship has experienced a substantial expansion in recent years, the existing body of literature is yet to explore the regional opportunities and challenges encountered within this nascent field. Through semi-structured interviews with 42 software representatives and using city, company, and human resource perspectives, this study aims to investigate the resource capabilities and major obstacles faced by software entrepreneurs in Izmir's software ecosystem. Results of our content analysis show that resource capabilities and opportunities include an intention to collaborate, a positive city image, and a skilled workforce whereas challenges involve the absence of a robust investment culture, brain drain, and bureaucratic impediments. The potential and obstacles faced by software entrepreneurs in Izmir vary depending on the level of analysis, according to findings at the city, company, and human resource levels. By examining the resource capacities and challenges within Izmir's software entrepreneurship ecosystem, with a focus on identifying the software-related strengths and barriers that shape an innovative urban environment supportive of entrepreneurial efforts, our research highlights both external factors that impact software investments and internal conditions enhancing Izmir's attractiveness as a business hub. The findings suggest that Izmir's software ecosystem has substantial potential to drive the city's economic growth and position itself as a key contributor to urban innovation. Building on previous research that underlines the importance of clusters in promoting entrepreneurship and creating regional business opportunities, our study indicates a promising opportunity to foster software company clusters in Izmir. The study provides recommendations tailored for policymakers and industry leaders with an interest in advancing the software entrepreneurship ecosystem in Izmir as well as analogous emerging country ecosystems.
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    A Study on the Effects of Loneliness, Depression and Perceived Social Support on Problematic Internet Use among University Students
    Ozsaker, M; Muslu, GK; Kahraman, A; Beytut, D; Yardimci, F; Basbakkal, Z
    The present study investigated the effects of loneliness, depression and perceived social support on problematic Internet use among university students. The participants were 459 students at two universities in Turkey. The study data were collected with a Questionnaire Form, Problematic Internet Use Scale (PIUS), University of California at Los Angeles (UCLA) Loneliness Scale (Version 3), Multidimensional Scale of Perceived Social Support (MSPSS) and Beck Depression Inventory (BDI). The Mann-Whitney U Test and Kruskal-Wallis one-way analysis of variance were conducted to examine the differences; and correlation and regression analyses were used to examine the relationships between variables. There was a positive significant correlation between the PIUS and MSPSS and the UCLA Loneliness Scale and a negative significant correlation between the PIUS and Beck Depression Scale (BDS). The female students had higher total PIUS scores. The results also illustrated that there was a statistically significant difference in total PIUS scores according to having a social network account.
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    Investigating barriers to circular supply chain in the textile industry from Stakeholders' perspective
    Kazancoglu, I; Kazancoglu, Y; Kahraman, A; Yarimoglu, E; Soni, G
    The objectives of this study are to understand the circular supply chain barriers for textile companies to implement the circular economy. Main contributions of the study were to propose a specific framework that reveals circular supply chain barriers in transition to circular economy with holistic view by encompassing all stakeholders, to reveal causal relationships among the circular supply chain barriers within textile industry. Causal relationships between the proposed circular supply chain barriers were identified by Fuzzy-Decision Making Trial and Evaluation Laboratory (DEMATEL) method. The barriers are classified under cause and effect groups and related implications are proposed. The findings of this study are lack of collecting, sorting and recycling, reluctance for acceptance of CE model, and problems related to uniformity and standardisation are revealed as the most important barriers, respectively. Moreover, lack of technical knowledge is the most influencing factor, whereas, challenges in product design is the most influenced factor.
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    Detecting fake reviews through topic modelling
    Birim, SÖ; Kazancoglu, I; Mangla, SK; Kahraman, A; Kumar, S; Kazancoglu, Y
    Against the uncertainty caused by the information overload in the online world, consumers can benefit greatly by reading online product reviews before making their online purchases. However, some of the reviews are written deceptively to manipulate purchasing decisions. The purpose of present study is to determine which feature combination is most effective in fake review detection among the features of sentiment scores, topic distributions, cluster distributions and bag of words. In this study, additional feature combinations to a sentiment analysis are searched to examine the critical problem of fake reviews made to influence the decision-making process using review from amazon.com dataset. Results of the study points that behavior-related features play an important role in fake review classifications when jointly used with text-related features. Verified purchase is the only behavior related feature used comparatively with other text-related features.
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    The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods
    Birim, S; Kazancoglu, I; Mangla, SK; Kahraman, A; Kazancoglu, Y
    In recent years, machine learning models based on big data have been introduced into marketing in order to transform customer data into meaningful insights and to make strategic decisions by making more accurate predictions. Although there is a large amount of literature on demand forecasting, there is a lack of research about how marketing strategies such as advertising and other promotional activities affect demand. Therefore, an accurate demand-forecasting model can make significant academic and practical contributions for business sustainability. The purpose of this article is to evaluate machine learning methods to provide accuracy in forecasting demand based on advertising expenses. The study focuses on a prediction mechanism based on several Machine Learning techniques-Support Vector Regression (SVR), Random Forest Regression (RFR) and Decision Tree Regressor (DTR) and deep learning techniques-Artificial Neural Network (ANN), Long Short Term Memory (LSTM),-to deal with demand forecasting based on advertising expenses. Deep learning is a powerful technique that can solve marketing problems based on both classification and regression algorithms. Accordingly, a television manufacturer's real market dataset consisting of advertising expenditures, sales and demand forecasting via chosen machine learning methods was analyzed and compared in terms of the accuracy of demand forecasting. As a result, Long Short Term Memory has been found to be superior to other models in providing highly accurate prediction results for demand forecasting based on advertising expenses.