TR-Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1927
Browse
3 results
Search Results
Article Central Bank Digital Currency in an Emerging Market Economy: Case of the Central Bank of the Republic of Türkiye(2024) Asfuroğlu, DilaCentral banks have historically been using traditional channels for communication and physical money for transactions, fulfilling the needs of previous millennia, while the evolution of technology, electronic devices, and needs in transactions necessitate the use of modern communication channels, such as X (formerly known as Twitter), as well as modern payment systems, such as central bank digital currencies (CBDC). Hence, this paper aims to unfold where the Central Bank of the Republic of Türkiye (CBRT), as an example of emerging markets, stands in informing the public about CBDC. To this end, it conducts an event study on the official X account of CBRT in English over 10.2020-12.2022 by utilizing Nvivo. The findings of the quantitative analysis of the tweets show that CBRT does not regard X as a primary communication channel and mainly shares links to publications from the official websites in X. Also, CBRT tends to adopt a ‘cold-turkey’ informative approach about CBDC with the public rather than ‘gradualism’. Consequently, CBRT should rigorously design a communication strategy that fulfills the needs of the modern economy and start addressing CBDC to raise awareness if a quick transition to digital currency is targeted.Article Innovation and Productivity Research in the Last Five Decades: a Bibliometric Analysis(2024) Çelik, DeryaPurpose: This study aims to reveal research trends by revealing the evaluation in this field by making a holistic analysis of academic studies that have examined the concepts of innovation and productivity in the last five decades. This analysis aims to reveal the general structure of academic studies that deal with the concepts of innovation and productivity. Methodology: Articles searched in the ‘‘Social Science Citation Index (SSCI)’’, ‘‘Science Citation Index Expanded (SCI-EXPANDED)’’ and ‘‘Emerging Sources Citation Index (ESCI)’’ in the ‘‘Web of Science (WoS)’’ database, researching innovation and productivity together between 1980-2023. It was analysed and mapped using the VOSviewer 1.6.19 software and manual methods. Co-occurrence Keyword Analysis, Document Co-citation Analysis and manual analysis methods were used in the mapping. Findings: This study reveals how research in innovation and productivity has developed over the last five decades and what trends it has. It has been determined that the most published areas are Economy, Management and Business. The most frequently used keywords were found to be "innovation", "productivity", "research-and-development", "growth", "performance" and "impact". The most published topics on a cluster basis are "impact", "innovation and productivity", "growth", "research and development" and "performance", respectively. In the document co-citation analysis, it was determined that the publication in which all publications were linked included the study titled "Research, Innovation and Productivity: an econometric analysis at the firm level", published by Crépon et al. (1998). This information can be a valuable resource for future research and policy-making and can be used to drive innovation and productivity progress. Originality: While the study is the first and only content analysis to reveal the combined trends in this field by examining the "innovation and productivity" studies together, it is thought that the results obtained can guide researchers and professionals.Article Neural Coreference Resolution for Turkish(2023) Demir, ŞenizCoreference resolution deals with resolving mentions of the same underlying entity in a given text. This challenging task is an indispensable aspect of text understanding and has important applications in various language processing systems such as question answering and machine translation. Although a significant amount of studies is devoted to coreference resolution, the research on Turkish is scarce and mostly limited to pronoun resolution. To our best knowledge, this article presents the first neural Turkish coreference resolution study where two learning-based models are explored. Both models follow the mention-ranking approach while forming clusters of mentions. The first model uses a set of hand-crafted features whereas the second coreference model relies on embeddings learned from large-scale pre-trained language models for capturing similarities between a mention and its candidate antecedents. Several language models trained specifically for Turkish are used to obtain mention representations and their effectiveness is compared in conducted experiments using automatic metrics. We argue that the results of this study shed light on the possible contributions of neural architectures to Turkish coreference resolution.