Mehmet Nafiz Aydın

Aydın, Mehmet Nafiz

Name Variants

Mehmet Nafiz, Aydin
MEHMET NAFIZ AYDIN
Aydın, MEHMET NAFIZ
Mehmet Nafiz AYDIN
AYDIN, MEHMET NAFIZ
Mehmet Nafiz Aydın
Aydın, M.
Aydin,M.N.
Aydin M.
Aydin,Mehmet Nafiz
Aydin, Mehmet Nafiz
A., Mehmet Nafiz
Aydın, M. N.
Aydın,M.N.
Aydın, Mehmet Nafiz
Nafiz Aydin M.
M. Aydın
M. N. Aydın
AYDIN, Mehmet Nafiz
Aydın M.
A.,Mehmet Nafiz
Aydin, Mehmet
Aydin, Mehmet N.
Aydın, M.N.

Job Title

Doç. Dr.

Email Address

mehmet.aydin@khas.edu.tr

ORCID ID

0000-0002-3995-6566

Scopus Author ID

8873732700

Full item page

Scholarly Output

50

Articles

21

Citation Count

127

Supervised Theses

14 Scholarly Output Search Results

Now showing 1 - 10 of 50

Citation Count: 0
Metadata Action Network Model for Cloud Based Development Environment
(Springer international Publishing Ag, 2020) Aydin, Mehmet N.; Perdahci, Ziya N.; Safak, I; van Hillegersberg, J. (Jos)
Cloud-based software development solutions (entitled as Platformas-a-Service, Low-Code platforms) have been promoted as a game changing paradigm backed by model-driven architecture and supported by various cloud-based services. With the engagement of a sheer number of platform users (experienced, novel, or citizen developers) these platforms generate invaluable data and that can be considered as user metadata actions. As cloud-based development solutions provide novice users with a new development experience (performing data actions that altogether leads to a successful software app), users often times face with uncertainty about development performance; how good or complete is app development? Thus, the issue addressed in this research is how to measure user performance by using digital trace data generated on the cloud platform from a Network Science perspective. This research proposes a novel approach to leveraging digital trace data on Platform-as-a-Service (PaaS) from a Network Science perspective. The proposed approach considers the importance of digital trace data as metadata actions on PaaS and introduces a network model (so-called Metadata Action Network), which is claimed to be the result of reconstruction of events of developer's actions. We show suitability of the proposed approach to better understanding of real-world digital trace data on PaaS solution and elaborate basic performance analytics on a PaaS solution with research and practical implications.
Social Network Analysis of Innovation Mentor Community of Practice
(Kadir Has Üniversitesi, 2022) ALTINIŞIK, Gunda Esra; Aydın, Mehmet Nafiz
Innovation is directly related to the development of economies, and with the awareness of its criticality, various nation-wide support programs and innovation communities have emerged in recent years. These communities are established along their own specific structures and dynamics that can be examined by their level of connectedness and its underlying members’ attributes. In this research, a government-sponsored innovation mentors’ community of practice (CoP) has been examined. Thus, the members are advised to bring their knowledge to adopt the framework to specific cases and share their experiences with their peers. A CoP stands on the basic premise that the practice (knowhow) is shared among members and stimulates connectedness along their competencies. In this context, the first question is: how to measure the connectedness of the community and whether the CoP under investigation achieves the desired level of connectedness? The second is: what specific mentors’ attributes (competencies) characterize the preferred choices of connectedness? More particularly, how knowledge-sharing preferences are associated by the mentors’ attributes of this CoP? We employed Social Network Analysis techniques and Jaccard Similarity to answer them. The findings reveal that the CoP of innovation mentors is highly connected for a giant component, but low at the network level. Degree, title and institution as the members’ attributes may not play a significant role in the connectedness of this community. Even though mentors meet on a denominator in basic competencies in their cooperation, the findings show that they cooperate interdisciplinary. We argue that the dissimilar competencies of the connected mentors can be considered as a signature of the very idea of connectedness. Further research is needed to validate this claim with richer data, preferably with a temporal aspect.
Modularity Analysis of a Health Information Platform From a Network Science Perspective
(Kadir Has Üniversitesi, 2015) Alaşan, Semiha Nur; Aydın, Mehmet Nafiz
As Barabási (2015) emphasizes that the very idea of communities goes back to the time people were born into communities and had to find their individuality. But today it is the other way around, that is, people are born individuals and have to find their communities. This research is aimed to better understand modularity measures for a health information platform. Such platform is an exemplary of its many kinds that are characterized as interactive, web-based information exchange platforms. These platforms draw attentions of academics due to its underlying complex systems behavior in various domains such as computer science, management science, sociology, and management information systems (MIS). We adopt a perspective of an emerging interdisciplinary field called network science that enables us to analyze modularity of a health information platform. The whole connection network normally has 2143 nodes and 5706 edges. In this research, since the modularity is examined, it is vague to integrate the sprinkled nodes over the giant component. Thus and so, the only giant component having 1652 nodes and 5146 edges is studied. This analysis is based on the modularity maximization algorithm, which Gephi uses as default, by assigning two different resolution values for the same data set to try which resolution value gives better results.
Citation Count: 0
Innovation mentor community of practice: a social network analysis perspective
(Emerald Group Publishing Ltd, 2023) Altinisik, Gunda Esra; Aydin, Mehmet Nafiz
PurposeTo exploit collaboration-driven innovation, in recent years, many government-sponsored innovation programs and mentor services have emerged. These services support an effective exchange of knowledge among innovation actors, including innovation mentors and enable mentor connectedness as an important factor to develop and sustain effective innovation mentors' community of practice (CoP). The purpose of this paper is to examine the degree of connectedness in an innovation mentor CoP. Design/methodology/approachIn this study, the innovation mentors CoP as part of a national innovation program is considered a network. The connectedness and assortative mixing of this CoP and the effects of these two on each other were examined by using social network measures, including component analysis, the giant component (GC) and assortativity. FindingsThe authors provide the analytical interconnectedness results for both the GC and the whole network with network analysis and assortativity measurements of three attributes of mentors (institution, title and degrees). The degree of correlation of community for the GC shows preferential attachment between high-ranking and low-ranking mentors, while preferential attachment was not observed for the whole network. The correlation coefficient for the institution attribute has the highest value for GC, while the title has the highest value for the whole network. Originality/valueThe study is one of the early attempts to apply social network analysis for an innovation mentor CoP. This study reveals the criticality of evaluating the GC and the whole network separately and provides a number of research and practical directions that will contribute to the development of the innovation mentor CoP.
Citation Count: 1
Network analysis of innovation mentor community of practice
(Emerald Group Publishing Ltd, 2023) Altinisik, Gunda Esra; Aydin, Mehmet Nafiz; Perdahci, Ziya Nazim; Pasin, Merih
PurposePositive effect of knowledge sharing (KS) on innovation has come to the fore and government-supported innovation and mentoring communities or mentor networks have become widespread. This article aims to examine the community connectedness and mentors' preferences for professional competency-based KS of such innovation community of practice networks (CoPNs).Design/methodology/approachThe paper constructs a directed weighted CoPN model with a node-attribute-based novel fingerprint edge weights. Based on the CoPN, Social Network Analysis (SNA) metrics and measures including Giant Component (GC) were proposed and analyzed to identify mentors' connectedness preferences. The fingerprint was proposed as a novel binarized node attribute of competence. Jaccard similarity of fingerprints was proposed as edge weights to reveal correlations between competences and preferences for KS.FindingsThe work opted to conduct a survey of 28 innovation mentors to measure a CoPN. Both a name generator question and a second set of questions were employed to invite respondents to name their collaborators and indicate their professional competence. SNA metrics result in differing values for GC and the rest, which lead us to focus on GC to reveal salient metrics of connectedness. Jaccard similarity analysis results on GC demonstrate that mentors collaborate in an interdisciplinary manner.Originality/valueBased on the CoPN, the methods proposed may be effective in predicting preferred relationships for interdisciplinary collaborations, providing the managers with an analytical decision support tool for KS in practice.
Citation Count: 1
School-wide friendship metadata correlations
(Elsevier Ltd, 2019) Aydin,M.N.; Perdahci,Z.N.
Managers and education practitioners desire to know an extent to which sustainable school-wide friendship exists. Drawing on theory of network, this research focuses on bestfriendships that may contribute to positive school experience or school belonging in the context of school-wide interactions. We emphasize that school-wide unity is essential to refer to shared perceived friendship experience at the school level. The basic trust of this study is that managers should consider interconnectedness as a complex system of entangled interactions among students. We investigate best friendship network on the meso-to-macro scale. Particular attention is paid to the network phenomena of the largest component and network correlations for examining school-wide unity. The results show that abundance of asymmetric friendships leads to unity around school wide interactions. As suggested by network theory, popular students’ tendency to avoid forming closed clusters assures sustainability in school-wide friendships, and having same gender type or being classmates correlate highly with the choice of best friends, in contrast to achievement scores. Metadata correlations reveal same-gender and same-class clubs. Incorporating meso level findings into macro level indicates that some metadata (e.g. gender) may be considered as salient characteristics of the communities while other metadata (e.g. achievement scores) may be irrelevant. © 2018 Elsevier Ltd
Citation Count: 1
School-wide friendship metadata correlations
(Pergamon-Elsevier Science Ltd, 2019) Aydın, Mehmet Nafiz; Perdahçı, Nazım Ziya
Managers and education practitioners desire to know an extent to which sustainable school-wide friendship exists. Drawing on theory of network this research focuses on bestfriendships that may contribute to positive school experience or school belonging in the context of school-wide interactions. We emphasize that school-wide unity is essential to refer to shared perceived friendship experience at the school level. The basic trust of this study is that managers should consider interconnectedness as a complex system of entangled interactions among students. We investigate best friendship network on the meso-to-macro scale. Particular attention is paid to the network phenomena of the largest component and network correlations for examining school wide unity. The results show that abundance of asymmetric friendships leads to unity around school wide interactions. As suggested by network theory popular students' tendency to avoid forming closed clusters assures sustainability in school-wide friendships and having same gender type or being classmates correlate highly with the choice of best friends in contrast to achievement scores. Metadata correlations reveal same-gender and same-class clubs. Incorporating meso level findings into macro level indicates that some metadata (e.g. gender) may be considered as salient characteristics of the communities while other metadata (e.g. achievement scores) may be irrelevant.
Unveiling the Significance of Individual Level Predictions: a Comparative Analysis of Gru and Lstm Models for Enhanced Digital Behavior Prediction
(Mdpi, 2024) Kiyakoglu, Burhan Y.; Aydin, Mehmet N.
The widespread use of technology has led to a transformation of human behaviors and habits into the digital space; and generating extensive data plays a crucial role when coupled with forecasting techniques in guiding marketing decision-makers and shaping strategic choices. Traditional methods like autoregressive moving average (ARMA) can-not be used at predicting individual behaviors because we can-not create models for each individual and buy till you die (BTYD) models have limitations in capturing the trends accurately. Recognizing the paramount importance of individual-level predictions, this study proposes a deep learning framework, specifically uses gated recurrent unit (GRU), for enhanced behavior analysis. This article discusses the performance of GRU and long short-term memory (LSTM) models in this framework for forecasting future individual behaviors and presenting a comparative analysis against benchmark BTYD models. GRU and LSTM yielded the best results in capturing the trends, with GRU demonstrating a slightly superior performance compared to LSTM. However, there is still significant room for improvement at the individual level. The findings not only demonstrate the performance of GRU and LSTM models but also provide valuable insights into the potential of new techniques or approaches for understanding and predicting individual behaviors.
Citation Count: 0
INFLUENCE OF DIFFERENT THEORIES OF ETHICS ON ORGANIZATIONAL CODES OF CONDUCT OR ETHICS: A COMPARATIVE SEMANTIC ANALYSIS
(2022) Albayrakoglu, M. Murat; Aydın, Mehmet Nafiz
The aim of this study was to investigate the influence of various theories of ethics on codes of conduct or codes of ethics of computing and data organizations. To quantify and evaluate the differences in influence, four Python libraries, namely difflib, gensim, nltk, and spaCy, and, in addition, a web-based proprietary semantic similarity tool, Compare Text, were used. The codes of seven computing and data organizations for Information Technology (IT) professionals and scholars were compared to the descriptions of five different schools of ethical thought through four different tools. The findings were tabularized, summarized in radar charts, and their implications were discussed: It was found that there are some differences of influence on the codes by different theories. However, the percentages of similarities calculated by each tool were observed to differ, on some occasions, considerably. Finally, contributions and limitations of the current work and recommendations for further studies were presented.
Citation Count: 2
Discovering Customer Purchase Patterns in Product Communities: An Empirical Study on Co-Purchase Behavior in an Online Marketplace
(Mdpi, 2021) Kafkas, Kenan; Perdahci, Ziya Nazim; Aydin, Mehmet Nafiz
Marketplace platforms gather and store data on each activity of their users to analyze their customer purchase behavior helping to improve marketing activities such as product placement, cross-selling, or customer retention. Market basket analysis (MBA) has remained a valuable data mining technique for decades for marketers and researchers. It discovers the relationship between two products that are frequently purchased together using association rules. One of the issues with this method is its strict focus on binary relationships, which prevents it from examining the product relationships from a broader perspective. The researchers presented several methods to address this issue by building a network of products (co-purchase networks) and analyzing them with network analysis techniques for purposes such as product recommendation and customer segmentation. This research aims at segmenting products based on customers' purchase patterns. We discover the patterns using the Stochastic Block Modeling (SBM) community detection technique. This statistically principled method groups the products into communities based on their connection patterns. Examining the discovered communities, we segment the products and label them according to their roles in the network by calculating the network characteristics. The SBM results showed that the network exhibits a community structure having a total of 309 product communities, 17 of which have high betweenness values indicating that the member products play a bridge role in the network. Additionally, the algorithm discovers communities enclosing products with high eigenvector centralities signaling that they are a focal point in the network topology. In terms of business implications, segmenting products according to their role in the system helps managers with their marketing efforts for cross-selling, product placement, and product recommendation.