Πλοήγηση ανά Επιβλέπων "Mamakou, Xenia"

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Α Β Γ Δ Ε Ζ Η Θ Ι Κ Λ Μ Ν Ξ Ο Π Ρ Σ Τ Υ Φ Χ Ψ Ω

Τώρα δείχνει 1 - 2 από 2

Cloud adoption: a new challenge that companies have to face
(03/16/2022) Barmpa, Melina; Μπάρμπα, Μελίνα; Athens University of Economics and Business, Department of Business Administration; Mamakou, Xenia
The purpose of this study is to identify and assess the factors that may influence a company's decision to adopt cloud computing in the Greek market. This study firstly discusses some of the major challenges and difficulties for cloud computing from an organizational and technical perspective. Various research challenges relating to the following topics are discussed: the organizational changes brought about by cloud computing; the economic and organizational implications of its utility billing model; the security, regulatory, business continuity and privacy issues that cloud computing raises, as well as the trust that company and cloud provider should develop. Then, the various approaches and theories that have been used in order to evaluate Cloud Adoption are analyzed and for this research, a framework that combines both TOE and cost factors is selected. Considering an already adopted framework, a questionnaire was designed and completed by the management of a variety of companies in Greece. Then, a qualitative analysis of the results was performed according to PLS-SEM methodology via the SmartPLS tool.The study results determined that management’s perception of technical and environmental factors significantly influences the adoption of cloud computing. Moreover, the result of the study also indicates that regarding the technical factor, security is one of the significant factors that influence this decision both in terms of the measures and the mechanisms that cloud providers have in place. Regarding environmental factors, as one or more cloud providers provide the Cloud computing solutions, the cloud provider’s expertise in the industry’s solutions, the references that may have as well as the number and the variety of the cloud providers that offer adequate services have a significant impact on the company's decision.
Performance evaluation of research publications through text mining applications
(2020) Kyriakoulea, Sofia; Κυριακουλέα, Σοφία; Athens University of Economics and Business, Department of Business Administration; Manolopoulos, Dimitrios; Fraidaki, Aikaterini; Mamakou, Xenia
Research, as an activity of academia, has been growing over the past decades and that results to an increased number of scientific publications hosted in the numerous Journals that are available to publish today. The competition between researchers, universities and institutes is fierce. That raises concerns whether the published literature should be viewed as a product. To keep up with the ever-growing literature collection new age data science comes in to assist. Text Mining is one of the available technologies that could aid in the analysis of millions of texts, in their categorization and even assist the already existing heuristic algorithms to increase visibility of articles that otherwise would be left in the dark. Without having to manually read any document in the corpus of the present study, it was possible to extract information on the most common topics by defining the keywords in the collection. Term Frequency (TF) and the product of TF and Inverse Document Frequency (IDF) were used as ways to identify the keywords. In addition, other data were collected for the corpus such as the publication year, the number of authors, the issue number of the Journal, the length of the article in pages, the number of cited refences and the term count. All these variables functioned as predictors of lifetime and yearly average citations of the collection. There were two types of analysis preformed, besides some descriptive statistics of the collection. The first type was two linear regressions with the dependent variables being the lifetime and yearly average citations. The aim of the analysis was to determine which of the collected variables could predict the efficiency of an article in terms of citations, in essence what makes them publicly appealing.