Ph.D. Theses
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Item Comparison of investor behavior between real and simulated trading environments(Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in the Social Sciences, 2023., 2023) Şenol, Doğaç.; Onay, Ceylan.Investment behavior between real and simulated trading environments are compared and whether gamification can help mitigate behavioral biases of investors is investigated by conducting a unique stock market experiment that is free from observer-expectancy and subject-expectancy effects. Utilizing the trading data of investors who simultaneously have active portfolios in an investment firm and stock market simulation game, it is shown that investors have different biases in real versus simulated settings. Participating in a stock market game is found to be affecting all biases differently, with different degrees of participation to the game. While overconfidence bias and disposition effect can be mitigated and decrease with more active participation in the game, familiarity and status quo biases increase. It is also shown that young, inexperienced investors with average-sized portfolios and men are more likely to participate. These findings will especially be of interest to researchers, financial institutions and policy makers that plan to conduct similar experiments and design services promoting better financial decision-making and investment behavior.Item Using blockchain technology to improve the collaboration in trucking industry : a design science research(Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in the Social Sciences, 2023., 2023) Alaçam, Serkan.; Sencer, Aslı.The global trucking industry has been suffering from chronic problems for a long time, such as high levels of carbon emissions, driver shortages, and extended payment wait times. This study acknowledges the need for more innovative technology solutions to address those problems based on trucking industry reports. We investigate how blockchain technology can eliminate or reduce the role of intermediaries in trucking operations and improve collaboration among shippers and carriers. In our design constructs, we extend the transportation control tower concept from the literature by operationalizing it in a decentralized fashion on the consortium and public blockchain architectures. We evaluate the technical feasibility of our design constructs with experienced blockchain engineers. In addition, we review our design constructs with trucking industry professionals to assess how successful they would be in removing the barriers to collaboration among shippers and carriers. The evaluation results show that shippers and medium and large carriers are interested in using blockchain technology to eliminate trust- related concerns and execute more collaborative trucking operations. Even though owner-operators prefer consortium blockchains over public blockchains due to competition concerns, they are still willing to join public blockchains for finding loads and managing trucking operations if shippers prefer using public blockchains over consortium blockchains. On the other hand, as an emerging technology, blockchain needs time to address critical problems such as identity management, scalability, the privacy of transactions, and compliance with data protection regulations.Item Predicting the winning team in basketball : a complex systems approach(Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in the Social Sciences, 2023., 2023) Ösken, Latif Cem.; Onay Şahin, Ceylan.Predicting the winner of a basketball game is a difficult task, due to the inherent complexity of team sports. All 10 players on the court interact with each other and this intricate web of relationships makes the prediction task difficult, especially if the prediction model aims to account for how different players amplify or inhibit other players. Building our approach on complex systems and prototype heuristics, we identify player types through clustering and use cluster memberships to train prediction models. We achieve a prediction accuracy of ~76% over a period of five NBA seasons and a prediction accuracy of ~71% over a season not used for model training. Our best models outperform human experts on prediction accuracy. Our research contributes to the literature by showing that player stereotypes extracted from individual statistics are a valid approach to predict game winners.Item Examination of multimedia learning principles in augmented reality and virtual reality learning environments(Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in the Social Sciences, 2023., 2023) Çeken, Burç.; Taşkın, Nazım.This study examined the effects of augmented reality (AR), virtual reality (VR), and traditional learning environments on university students' cognitive load, motivation, and learning outcomes. The efficacy of segmenting, pre-training, and modality principles was also investigated in reducing cognitive load and improving learning outcomes within these contexts. A 3x4 factorial design was implemented with 383 participants, assessing retention, transfer, cognitive load, and motivation scores. Results showed that AR significantly improved retention for cell structure compared to traditional learning, while no significant differences were found for lightning formation. The modality effect was observed for lightning formation in AR but not for cell structure. The segmenting effect was present for retention in both subjects but absent for transfer and cognitive load scores. The pre-training effect was observed for retention in VR and AR for lightning formation but not for cell structure, with inconsistent results for transfer scores. These findings suggest that various factors, including subject matter complexity, learning environment characteristics, instructional design, and individual learner differences, influence the presence or absence of these effects. This highlights the importance of considering these factors when designing educational interventions to optimize learning outcomes. Further research must identify conditions for effectively leveraging these effects across different subject matters, learning environments, and outcomes.Item Consumers' attitudes toward and intentions to adopt smart home technologies(Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in the Social Sciences, 2023., 2023) Başarır, Birgül.; Nasır, Aslıhan.; Türker, Hande.Smart homes embrace different smart home technologies (SHT) to offer various services to fulfill their users’ needs and wants. They are one of the key enablers of smart living. Conversely, SHT penetration is still growing slowly compared to its potential benefits. Thus, this dissertation proposes a diffusion of innovation (DOI) based integrated research model for understanding consumers’ attitudes toward and intentions to adopt smart home technologies, through subjective perceptions of the innovation characteristics. For this purpose, the existing literature on technology adoption and smart homes is examined and innovation characteristics are complemented with the contextual factors extracted from 13 expert interviews. The final research model is tested with the data collected from 995 individuals via a face to-face survey. During the analysis, consumers are clustered into meaningful segments regarding their technology-related traits stemming from the technology readiness index (TRI) 2.0, and smart home adoption determinants are explored for these consumer segments to reveal segment-based distinctions. Additionally, differences in SHT adoption are investigated based on lifestyle attributes-based consumer segments, demographics, socioeconomics, prior experience, and housing structure. This research contributes to understanding the adoption of innovations in the consumer behavior context.Item A 360° customer lifetime value prediction method using machine learning for multi category e-commerce companies(Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in the Social Sciences, 2023., 2023) Yılmaz, Gülşah.; Badur, Bertan Yılmaz.; Mardikyan, Sona Kunuzyan.The Customer Lifetime Value (CLV) prediction methods that are used by e-commerce companies are mainly focusing on a specific group of customers with multiple transaction data and therefore remain unable to value the one-time purchasers. As a result, the management of these companies is incapable of valuing all customers and the overall company. Thus, each type of user needs a different way to predict CLV. In this thesis, we intend to develop a novel 360° holistic technique for the prediction and use of CLV models in the marketing management of multi-category e-commerce companies to enhance their strategic decision-making. The research compared the proposed framework which was constructed with several outputs (CLV, DPC, and TAS) with other ML models to evaluate the new variables created based on relationship marketing theory (RMT) and to demonstrate the best model which is more appropriate for multi category e-commerce companies' usage. To make this result useful, we created customer clusters that enable management to separate end-users according to the three outputs. Finally, Shapley values obtained using explainable artificial intelligence (XAI) are then utilized to understand the DNN's findings. The results showed that using XAI shows which factors are more crucial to the results. Overall, the proposed model helps marketing management teams in planning their operations efficiently by differentially allocating their resources to specific types of customers based on their profitability which provides more strategic decision-making.Item An agent-based phishing attack model from a human-organizational- technical perspective(Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in the Social Sciences, 2023., 2023) Mutlutürk, Meltem Emine.; Metin, Bilgin.In the rapidly evolving digital landscape, cybersecurity has emerged as a significant concern for organizations. This thesis delves into the intricate dynamics of malware-based phishing attacks on enterprise computer networks. Utilizing the robust methodological tool of Agent-Based Modelling (ABM), the research is firmly rooted in socio-technical theory and the concept of complex adaptive systems (CAS). The study meticulously examines the pivotal role of human factors, particularly awareness training and the credibility of phishing emails, in determining susceptibility to phishing attacks. Also, it underscores the significant impact of technological countermeasures, including the strategic deployment of Endpoint Detection and Response (EDR) solutions and the implementation of a hybrid antivirus scan policy, in mitigating infection rates. By seamlessly integrating human behaviour with socio- technical dimensions, the research provides a nuanced, comprehensive understanding of cybersecurity threats. The findings underscore the necessity for a balanced, holistic approach that equally prioritizes human behaviour and technological measures. This approach is crucial to enhance organizational resilience against relentless cyber threats. The insights gained from this research offer invaluable guidance for organizations striving to navigate the complex cybersecurity challenges in today's increasingly digital age.Item Comparison of base-model and role-based model in virtual co- creation environments : innovations in healthcare industry(Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in the Social Sciences, 2023., 2023) Öztürk, Elif.; Türker, Hande.; Nasır, V. Aslıhan.Intense competition and technological advances lead companies to adopt open business models and cooperate with consumers to accelerate their innovation processes. Although this cooperation can lead to favorable results, some open innovation initiatives fail due to poor management of the co-creation process. This study aims to explore the inputs of a successful co-innovation platform and examine which instruments will yield valuable contributions and high motivation for all partners in the long run. A multi-method study was conducted including a web-based experiment with 406 participants generating and evaluating ideas in the context of healthcare innovations and a survey capturing perceived benefits, overall experience, and future intention to participate. The results demonstrate that domain knowledge is an indication of contribution quality and innovativeness is an indication of contribution quantity. Although task differentiation improves hedonic and cognitive benefits, overall experience, and future intention to participate, task limitations on idea generation have a negative impact on social and personal benefits. This study provides insights into the impact of customized task design on the performance of co-innovation platforms from company and consumer perspectives and provides a guide for managers to build better consumer empowerment strategies in open innovation projects.Item Developing a dynamic predictive policing system(Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in the Social Sciences, 2022., 2022.) Hakyemez, Tuğrul Cabir.; Badur, Bertan Yılmaz.The retrospective predictive policing techniques are atheoretical and therefore remain incapable of sensing the changing crime risk across the streets. In this study, we aim to develop a dynamic predictive policing system that capitalizes on theory-based risk indicators. The sample includes all the theft and robbery incidents in Chicago between 2014-2019. In the first step, pipelining bivariate network K analysis and segmented regression, we introduce novel distance-aware risk functions that operationalize spatiotemporal crime risk around the selected urban features (i.e., bus stop, fast food restaurant, gas station, grocery store, pub). In the second step, we develop various network-based predictive policing methods using graph-based deep learning algorithms (i.e., GraphWavenet, Spatiotemporal Graph Convolutional Networks). These methods generate weekly and intraday hotspot predictions. We complement these methods with various theory-based risk indicators including a risk score devised from the novel risk functions, 311 calls, park events, and cooccurring crime incidents. The results showcase that crime risk around urban features varies across space, time, and crime types. Furthermore, this risk is found to be significantly correlated with the regional socioeconomic characteristics. Another important result shows that incorporating theory based indicators improved the performance of the retrospective methods up to 68%. Amongst the algorithms, GraphWavenet is found to outperform its counterparts in the majority of the prediction models with an accuracy as high as 80%. The proposed system helps law enforcement agents in planning their operations efficiently by pinpointing the micro geographical units with relatively higher risks in the next time step.Item Essays in learning representations of complex networks(Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in the Social Sciences, 2021., 2021.) Gürsoy, Furkan.; Badur, Bertan Yılmaz.This thesis contains three essays in learning representation of complex networks, the first two of which develop new methods and the third utilizes these methods in a real-world application. The first essay provides methods for extracting underlying signed network backbones from intrinsically dense weighted networks. Utilizing a null model based on statistical techniques, we propose significance and vigor filters that enable inferring edge signs and weights. Empirical analysis on four real-world networks reveals that the proposed filters extract meaningful and sparse signed backbones that exhibit characteristics typically associated with signed networks while respecting the multiscale nature of the network. The second essay deals with the misalignment problem in dynamic representation learning. We provide the first formal definitions of alignment and stability, propose novel metrics for measuring them, and show their suitability through a set of synthetic and real-world experiments. We show that, by ensuring alignment, the performance of dynamic network inference tasks improves by a remarkable amount. The third essay applies the novel methods developed in the first two essays as well as other methods from the network analysis literature to investigate the structure and dynamics of internal migration in Turkey. In addition to providing unique and specific insights, we find that most migration links are geographically bounded with exceptions of cities with large economic activity, migration takes place in well-defined routes, counter-streams develop for major migration streams, and the migration system is largely stable over time; which are generally in line with classical migration laws.Item A social media big data mining framework for detecting sentiments in multiple languages(Thesis (Ph.D.)-Bogazici University. Institute for Graduate Studies in the Social Sciences, 2018., 2018.) Coşkun, Mustafa.; Özturan, Meltem.The popularity of social media platforms has generated a new social interaction environment thus a new collaboration network among individuals. These platforms own tremendous amount of data about users’ behaviors and sentiments. One of these platforms is Twitter, which provides researchers data potential of benefit for their studies. Based on Twitter data, in this study a multilingual sentiment detection framework is proposed to compute European Gross National Happiness (GNH). This framework consists of a novel data collection, filtering and sampling method, and multilingual sentiment detection algorithm for social media big data, and tested with nine European countries (United Kingdom, Germany, Sweden, Turkey, Portugal, Netherlands, Italy, France and Spain) and their national languages over six-year period. The reliability of the data is checked with peak/troughs comparison for special days from Wikipedia. The validity is checked with a group of correlation analyses with OECD Life Satisfaction survey reports’, currency exchanges, and national stock market time series data. Then, the European GNH map is drawn for six years. Lastly, an exploratory study for determining the relationships between users’ Twitter account features (number of tweets, number of followers etc.) and happiness polarities are analyzed. Main aim of this study is to propose a novel multilingual social media sentiment analysis framework for calculating GNH for countries and change the way of OECD type organizations’ survey and interview methodology. Also, it is believed that this framework can serve more detailed results (e.g. daily or hourly sentiments of society in different languages).Item A digital innovations-driven regeneration model and corporate sustainability(Thesis (Ph.D.)-Bogazici University. Institute for Graduate Studies in the Social Sciences, 2020., 2020.) Coşkun Setirek, Abide.; Tanrıkulu, Zuhal.The traditional ways of doing business have been changed by digital innovations such as the Internet of things, blockchain and digital currency, data analytics, artificial intelligence, robots, additive manufacturing, etc. Firms can stay competitive using the benefits of digital technologies. The spread of the coronavirus disease in 2019 (COVID-19) all over the world has created a better understanding of the importance of organizations’ ability to keep up with digital innovations. In this study, a method for digital innovations-driven business model regeneration is developed and a dynamic business model, which can also be used in the business model regeneration process, to examine the effects of digital innovation strategies on the corporate sustainability is proposed. For this purpose, the existing literature on the business model innovation and system dynamic are examined, and the empirical data are collected from 44 managers using semi-structured interviews to complement gaps in the literature. Moreover, the digital innovations-driven business model regeneration method, which is proposed in this study, is applied to a real case. This study extends the literature on the business model innovation and the dynamic business model. The study can provide strategy analysts and managers with an opportunity to analyze the effects of potential digital innovation strategies on their current business models and to explore the most effective digital innovation strategies in order to regenerate their business model to gain a competitive advantage over their competitors or to sustain their business in light of technological developments.Item Developing a context-aware location recommender system for location-based social networks(Thesis (Ph.D.)-Bogazici University. Institute for Graduate Studies in the Social Sciences, 2018., 2018.) Bozanta, Aysun.; Kutlu, Birgül.People think about where to go many times throughout their lives. Although it is a very rapid and repetitive decision, generally it is hard to choose suitable places from endless number of options for some specific circumstances. Recommender systems are supposed to help to deal with those issues and take appropriate actions. However, the location decision is different from other decisions like what to listen, buy, or read from various aspects. The popularity of location-based social networks has prompted researchers to study recommendation systems for location. Traditional recommendation algorithms have been used for location recommendation. When used separately, each venue recommendation system algorithm has drawbacks. Another issue is that the context information is not commonly used in venue recommendation systems. Time, distance and weather conditions have more impact on decisions about where to go than all other decisions. Another point that should not be disregarded is that the effects of those contextual variables differ from user to user. This study proposes a hybrid recommendation model that combines contextual information, user- and item-based collaborative filtering and content-based filtering. For this purpose, user visit histories, venue-related information and contextual information related to individual user visits were collected from Twitter, Foursquare, and Weather Underground. The proposed hybrid system is evaluated using both offline experiments and a user study. This proposed system shows better results than baseline approaches.Item The anatomy of an online social community network(Thesis (Ph.D.)-Bogazici University. Institute for Graduate Studies in the Social Sciences, 2018., 2018.) Akar, Ezgi.; Mardikyan, Sona Kunuzyan.The emergence of Web 2.0 has revolutionized the ways of communication on the Internet and has allowed people to form their virtual worlds involving online communities and social networks. People have started to generate their contents, share them, and communicate with each other in these communities and networks. In parallel to the generation of huge amount of contents in these platforms, these communities and networks have become valuable data sources for businesses. It is the fact that analysis of user content in online communities and social networks allows businesses to enhance their business value and achieve their goals. In this sense, to create and manage these communities successfully, managers need to understand how to motivate community members and keep them frequently involved. Therefore, this study employs social network analysis to map and understand the network structure of an online community and to detect sub-communities in it. Additionally, it explores and identifies user roles in an online community and proposes a research model that investigates members’ usage intentions of the community. The research model also analyzes the moderating effect of these investigated user roles on members’ usage intentions of the community. In this manner, this study combines various theories for a better understanding of what roles exist in online communities, what roles members prefer to adopt, what usage intentions members have by presenting a four-phase methodology. Additional to theoretical implications, the study also guides managers to develop motivational strategies to keep their members continually satisfied in online communities.