Aydın, Mehmet Nafiz
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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.
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
Main Affiliation
Management Information Systems
Status
Current Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals
15
LIFE ON LAND
0
Research Products
16
PEACE, JUSTICE AND STRONG INSTITUTIONS
1
Research Products
14
LIFE BELOW WATER
4
Research Products
6
CLEAN WATER AND SANITATION
0
Research Products
3
GOOD HEALTH AND WELL-BEING
7
Research Products
17
PARTNERSHIPS FOR THE GOALS
1
Research Products
4
QUALITY EDUCATION
2
Research Products
2
ZERO HUNGER
5
Research Products
10
REDUCED INEQUALITIES
2
Research Products
7
AFFORDABLE AND CLEAN ENERGY
0
Research Products
13
CLIMATE ACTION
0
Research Products
1
NO POVERTY
0
Research Products
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
8
Research Products
12
RESPONSIBLE CONSUMPTION AND PRODUCTION
1
Research Products
8
DECENT WORK AND ECONOMIC GROWTH
2
Research Products
11
SUSTAINABLE CITIES AND COMMUNITIES
0
Research Products
5
GENDER EQUALITY
0
Research Products
Documents
64
Citations
673
h-index
15
Documents
50
Citations
411
Scholarly Output
66
Articles
28
Views / Downloads
480/5026
Supervised MSc Theses
13
Supervised PhD Theses
5
WoS Citation Count
184
Scopus Citation Count
332
WoS h-index
8
Scopus h-index
10
Patents
0
Projects
0
WoS Citations per Publication
2.79
Scopus Citations per Publication
5.03
Open Access Source
33
Supervised Theses
18
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| Journal | Count |
|---|---|
| Applied Sciences | 4 |
| Computers and Electronics in Agriculture | 3 |
| Journal of research in business (online) | 2 |
| Alphanumeric Journal | 1 |
| Applied Sciences (Switzerland) | 1 |
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Article Citation - WoS: 21Citation - Scopus: 31Adoption of Mobile Health Apps in Dietetic Practice: Case Study of Diyetkolik(Jmır Publıcatıons, Inc, 130 Queens Quay E, 2020) Akdur, Görkem; Aydın, Mehmet Nafiz; Akdur, GizdemBackground: Dietetics mobile health apps provide lifestyle tracking and support on demand. Mobile health has become a new trend for health service providers through which they have been shifting their services from clinical consultations to online apps. These apps usually offer basic features at no cost and charge a premium for advanced features. Although diet apps are now more common and have a larger user base, in general, there is a gap in literature addressing why users intend to use diet apps. We used Diyetkolik, Turkey's most widely used online dietetics platform for 7 years, as a case study to understand the behavioral intentions of users. Objective: The aim of this study was to investigate the factors that influence the behavioral intentions of users to adopt and use mobile health apps. We used the Technology Acceptance Model and extended it by exploring other factors such as price-value, perceived risk, and trust factors in order to assess the technology acceptance of users. Methods: We conducted quantitative research on the Diyetkolik app users by using random sampling. Valid data samples gathered from 658 app users were analyzed statistically by applying structural equation modeling. Results: Statistical findings suggested that perceived usefulness (P<.001), perceived ease of use (P<.001), trust (P<.001), and price-value (P<.001) had significant relationships with behavioral intention to use. However, no relationship between perceived risk and behavioral intention was found (P=.99). Additionally, there was no statistical significance for age (P=.09), gender (P=.98), or previous app use experience (P=.14) on the intention to use the app. Conclusions: This research is an invaluable addition to Technology Acceptance Model literature. The results indicated that 2 external factors (trust and price-value) in addition to Technology Acceptance Model factors showed statistical relevance with behavioral intention to use and improved our understanding of user acceptance of a mobile health app. The third external factor (perceived risk) did not show any statistical relevance regarding behavioral intention to use. Most users of the Diyetkolik dietetics app were hesitant in purchasing dietitian services online. Users should be frequently reassured about the security of the platform and the authenticity of the platform's dietitians to ensure that users' interactions with the dietitians are based on trust for the platform and the brand.Article Citation - WoS: 16Citation - Scopus: 22Design and Implementation of a Smart Beehive and Its Monitoring System Using Microservices in the Context of Iot and Open Data(Elsevier Sci Ltd, 2022) Aydin, Sahin; Aydin, Mehmet NafizIt is essential to keep honey bees healthy for providing a sustainable ecological balance. One way of keeping honey bees healthy is to be able to monitor and control the general conditions in a beehive and also outside of a beehive. Monitoring systems offer an effective way of accessing, visualizing, sharing, and managing data that is gathered from performed agricultural and livestock activities for domain stakeholders. Such systems have recently been implemented based on wireless sensor networks (WSN) and IoT to monitor the activities of honey bees in beehives as well. Scholars have shown considerable interests in proposing IoT- and WSN-based beehive monitoring systems, but much of the research up to now lacks in proposing appropriate architecture for open data driven beehive monitoring systems. Developing a robust monitoring system based on a contemporary software architecture such as microservices can be of great help to be able to control the activities of honey bees and more importantly to be able to keep them healthy in beehives. This research sets out to design and implementation of a sustainable WSN-based beehive monitoring platform using a microservice architecture. We pointed out that by adopting microservices one can deal with long-standing problems with heterogeneity, interoperability, scalability, agility, reliability, maintainability issues, and in turn achieve sustainable WSN-based beehive monitoring systems.Doctoral Thesis Proposing a Model for Precision Management Supervised With Machine Learning in Livestock Management(Kadir Has Üniversitesi, 2021) Ödevci, Bahadır Baran; Emsen, Ebru; Aydın, Mehmet NafizThe global demand for meat is predicted to rise by 40% in the next 15 years, owing to an increase in the number of people adopting protein-richer diets, and technology solutions in agricultural and livestock production systems are likely to play a vital role in addressing this issue. On the other hand, while expanding meat output, it will be critical to discover ways to reduce livestock farming's environmental footprint and assure high levels of animal care and health. In this thesis, we aim to propose a model and approach along with a number of steps to follow for a livestock farm to adapt an information management system to attain optimum production efficiency. We are seeking answers to respond to the following research question: How can a livestock farm utilize information management systems for optimum efficiency? In order to expand the research on a specific livestock case study, we focus on intensively managed sheep for lamb production. However, the model and approach proposed in this thesis can be applicable to any livestock farming that aims to utilize information systems for precision management of farm operations. First, we reviewed scientific research related to long-standing, novel-technology, and data sensors with emphasis on data-information-knowledge-wisdom and decision-making processes and for intensively managed sheep for lamb production. Secondly, we addressed what data elements exist in the context of a livestock farm and how data elements in the context of livestock farms are associated. Special attention was given to the data model of the farm context for managerial precision livestock farming (PLF) systems. Thirdly, we proposed the decision-making points supervised by machine learning models in a PLF management information system for intensively managed sheep for lamb production. At this point, we developed and adapted a Mobile Sheep Manager Software (M-SMS) for a commercial lamb production model using an appropriate cloud architecture that collects and utilizes farm data and responds to the farm management with respect to insights into the operational and financial aspects of the farm. The technology identifies real-time alarms pertaining to animal welfare, health, environmental effects, and production on the farm and provides troubleshooting recommendations. We also looked at its suitability for user experience as well as its impact on farm profitability and sustainability. This research has shown that M-SMS combined with cloud services compounded with Predictive Analytics Services can fine-tune flock management and significantly improve operational excellence. According to the usability results, intensive sheep farmers had access to "point and click" solutions to keep legislative records, attain operational guidance and build flock performance data. Finally, we propose a model and steps to follow to adapt the information management system to any livestock management system in order to attain optimum efficiency. It was concluded that the architecture of this application can be easily adapted to other intensively managed livestock if the steps in this study are followed precisely.Article Citation - WoS: 3Citation - Scopus: 4Understanding Virtual Onboarding Dynamics and Developer Turnover Intention in the Era of Pandemic(Elsevier Science inc, 2024) Akdur, Gorkem; Aydin, Mehmet N.; Akdur, GizdemThis study examines the dynamics of virtual onboarding (VO) for Salesforce Commerce Cloud developers during the COVID-19 pandemic in a multinational software company. The newly developed Virtual Integration and Retention Framework (VIRF), which provides an improved understanding of VO, customized to the opportunities and challenges presented by the pandemic, is the fundamental concept of this study. A two-staged, higher-order constructed (HOC) quantitative research approach was used for the study, revealing a negative relationship between VO success and the challenges brought on by the pandemic. This emphasizes how difficult it can be to transition to remote work settings, especially regarding how operational effectiveness and employee well-being interact. Furthermore, the study demonstrates the positive connection between VO success and the delivery of technology and equipment during the pandemic. This result emphasizes how important logistical support is to the effectiveness of remote work arrangements. The study's key findings show positive impact of successful VO on developers' job satisfaction and workplace relationship quality (WRQ). Strong VO practices are essential to improve employee retention, as evidenced by the inverse correlation between these factors and turnover intentions. The study uses mediation analysis, with job satisfaction and WRQ acting as mediators, to further clarify how VO success influences turnover intentions. This study offers an in-depth understanding of VO practices during the pandemic. It discusses the future of remote work and onboarding procedures while navigating the immediate difficulties caused by the outbreak. The study emphasizes how important VO is for improving WRQ, decreasing turnover intentions of developers within the software company, and improving job satisfaction. These insights benefit organizations trying to improve developer integration and retention in changing work environments and improve their remote work strategies.Master Thesis Adoption of an Agile Approach To E-Commerce Software Development Projects(Kadir Has Üniversitesi, 2015) Bilen, Metin; Aydın, Mehmet NafizCompanies' business operation methods and processes have been vigorously affected by the reflection of electronic business to economy. Product-focused business model, which is executed by traditional companies, has been changed to customer-driven business model due to the speed and facility of communication among project members. The impact of end-users has been changed in a good way by the computers and mobile devices on e-commerce world. Customers have more power thanks to the increase of alternatives meeting the customer needs, pricing policy executed among competitors, competition to provide services, and ease of access to products. The power of customers has been increased by reason of being digital, and it conduced more demanding and hardly satisfied customer mass. Adopting information technologies to a business process, increasing productivity and speeding up the process has become the main purpose of e-commerce companies. The capability of parallel execution between processes implemented in software projects and e-commerce business process is a curiosity. This thesis raises the issue of how an agile software development method catches up the same tempo and the same success with the software projects business dynamics. In particular, this research aims to understand the practice of adapting an agile method to various e-commerce software development projects from a framework adopted in method engineering. The framework employs static and dynamic aspects of the fragments of a method along with artifacts and actors types. We conducted a case study in one of the leading e-commerce companies where many software development projects have been examined extensively. We found plenty of project characteristics with respect to business dynamics, project organizations, and product characteristics which have been essential to understand an extent to which the method fragments have been adapted. This research is an attempt to surface the context in which the method is adapted to the project or vice versa.Article Citation - WoS: 1Citation - Scopus: 1AI-Driven Predictive Maintenance for Workforce and Service Optimization in the Automotive Sector(MDPI, 2025) Yildirim, Senda; Yucekaya, Ahmet Deniz; Hekimoglu, Mustafa; Ucal, Meltem; Aydin, Mehmet Nafiz; Kalafat, IremVehicle owners often use certified service centers throughout the warranty period, which usually extends for five years after buying. Nonetheless, after this timeframe concludes, a large number of owners turn to unapproved service providers, mainly motivated by financial factors. This change signifies a significant drop in income for automakers and their certified service networks. To tackle this issue, manufacturers utilize customer relationship management (CRM) strategies to enhance customer loyalty, usually depending on segmentation methods to pinpoint potential clients. However, conventional approaches frequently do not successfully forecast which clients are most likely to need or utilize maintenance services. This research introduces a machine learning-driven framework aimed at forecasting the probability of monthly maintenance attendance for customers by utilizing an extensive historical dataset that includes information about both customers and vehicles. Additionally, this predictive approach supports workforce planning and scheduling within after-sales service centers, aligning with AI-driven labor optimization frameworks such as those explored in the AI4LABOUR project. Four algorithms in machine learning-Decision Tree, Random Forest, LightGBM (LGBM), and Extreme Gradient Boosting (XGBoost)-were assessed for their forecasting capabilities. Of these, XGBoost showed greater accuracy and reliability in recognizing high-probability customers. In this study, we propose a machine learning framework to predict vehicle maintenance visits for after-sales services, leading to significant operational improvements. Furthermore, the integration of AI-driven workforce allocation strategies, as studied within the AI4LABOUR (reshaping labor force participation with artificial intelligence) project, has contributed to more efficient service personnel deployment, reducing idle time and improving customer experience. By implementing this approach, we achieved a 20% reduction in information delivery times during service operations. Additionally, survey completion times were reduced from 5 min to 4 min per survey, resulting in total time savings of approximately 5906 h by May 2024. The enhanced service appointment scheduling, combined with timely vehicle maintenance, also contributed to reducing potential accident risks. Moreover, the transition from a rule-based maintenance prediction system to a machine learning approach improved efficiency and accuracy. As a result of this transition, individual customer service visit rates increased by 30%, while corporate customer visits rose by 37%. This study contributes to ongoing research on AI-driven workforce planning and service optimization, particularly within the scope of the AI4LABOUR project.Article Citation - WoS: 28Citation - Scopus: 47Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data Using Deep Learning: Early Detection of Covid-19 Outbreak in Italy(Ieee-Inst Electrıcal Electronıcs Engıneers Inc, 2020) Karadayı, Yıldız; Aydın, Mehmet Nafiz; Öğrenci, Arif SelçukUnsupervised anomaly detection for spatio-temporal data has extensive use in a wide variety of applications such as earth science, traffic monitoring, fraud and disease outbreak detection. Most real-world time series data have a spatial dimension as an additional context which is often expressed in terms of coordinates of the region of interest (such as latitude - longitude information). However, existing techniques are limited to handle spatial and temporal contextual attributes in an integrated and meaningful way considering both spatial and temporal dependency between observations. In this paper, a hybrid deep learning framework is proposed to solve the unsupervised anomaly detection problem in multivariate spatio-temporal data. The proposed framework works with unlabeled data and no prior knowledge about anomalies are assumed. As a case study, we use the public COVID-19 data provided by the Italian Department of Civil Protection. Northern Italy regions' COVID-19 data are used to train the framework; and then any abnormal trends or upswings in COVID-19 data of central and southern Italian regions are detected. The proposed framework detects early signals of the COVID-19 outbreak in test regions based on the reconstruction error. For performance comparison, we perform a detailed evaluation of 15 algorithms on the COVID-19 Italy dataset including the state-of-the-art deep learning architectures. Experimental results show that our framework shows significant improvement on unsupervised anomaly detection performance even in data scarce and high contamination ratio scenarios (where the ratio of anomalies in the data set is more than 5%). It achieves the earliest detection of COVID-19 outbreak and shows better performance on tracking the peaks of the COVID-19 pandemic in test regions. As the timeliness of detection is quite important in the fight against any outbreak, our framework provides useful insight to suppress the resurgence of local novel coronavirus outbreaks as early as possible.Conference Object Analysis and Implications of the Giant Component for an Online Interactive Platform(Int Business Information Management Assoc-IBIMA, 2016) Aydın, Mehmet Nafiz; Perdahci, N. ZiyaThis research is concerned with practical and research challenges related to understanding the nature of online interactive platforms. So-called network science is adopted to investigate the very nature of these systems as complex systems. In this regard we examine an online interactive health network and show that the interactive platform examined exhibits essential structural properties that characterize most real complex networks. We basically look into the largest connected component so-called a giant component (GC) to better understand how the representative network has established. In particular we apply dynamic network analysis to investigate how the GC has evolved over time. We identify a particular pattern towards emerging a GC. Implications of the patterns have been elaborated from a management perspective. We recommend that the basic stages of the emergence of the GC might be of interest to platform managers while evaluating performance of online platforms.Article Citation - Scopus: 1School-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 LtdDoctoral Thesis Framework Genesis: Yönetim Çerçeveleri ve Yönetim Sistem Standartları için Özgün Bir Kök Çerçeve(2025) Gerek, Yalçın; Aydın, Mehmet NafizBirbirinden farklı yönetim çerçevelerinin (YÇ), sistem standartlarının (YSS) ve kurumsal mimarilerin (KM) yaygınlaşması, operasyonel verimsizliklere, stratejik uyum eksikliğine ve kafa karıştırıcı bir 'Paradoks Sözlüğü'ne yol açan parçalanmış bir yapı oluşturmaktadır. Bu tez, organizasyonel yönetime birleştirme ve sistemik netlik getirmek üzere tasarlanmış yenilikçi bir kök çerçeve olan Framework Genesis'in (FG) geliştirilmesi ve keşifsel doğrulaması yoluyla bu kritik boşluğu ele almaktadır. Araştırmada, özel olarak geliştirilmiş yedi fazlı Geliştirilmiş Sezgisel Araştırma Yöntemi (EHRM) tarafından yönlendirilen bir tasarım bilimi yaklaşımı kullanılmıştır. Bu metodoloji, iki temel bileşenin geliştirilmesini sağlamıştır. Birincisi, mevcut YÇ'lerin ve YSS'lerin kapsamlı bir analizinden türetilen 48 temel bileşenden oluşan doğrulanmış bir taksonomi olan Bütünlüklü Yapı Taşı Taksonomisi (CBBT), ortak bir içerik sözlüğü sunmaktadır. İkincisi, sistemik bir meta-model olan Birleşik Mimari Çerçevesi (UAF), mimari yapıyı sağlamaktadır. Framework Genesis, bu bileşenleri entegre ederek organizasyonu, ideal bir mimari tasarım (EidosTwin) ve onun somut operasyonel gerçekliği (OusiaTwin) arasında felsefi temelli bir ikilik aracılığıyla kavramsallaştırmaktadır. Çerçeve ve amaca yönelik geliştirilmiş doğrulama araçları (FGMAPM, FGGIS, FGCMAM), kapsamlı ve karma yöntemli bir doğrulama stratejisine (FGVAL) tabi tutulmuştur. Bu strateji, mevcut standartlara karşı nicel bir boşluk analizi, 209 firmanın katıldığı bir olgunluk değerlendirme çalışması ve üç derinlemesine boylamsal vaka çalışmasını içermiştir. Bulgular, FG'nin kavramsal sağlamlığını teyit etmiş ve sistemik sorunları teşhis etme ile organizasyonel dönüşüme rehberlik etmedeki pratik faydasını göstermiş, müdahale sonrası olgunluk skorlarında ölçülebilir iyileşmeler ortaya koymuştur. Bu tez, yönetim çerçevesi teorisi için yeni bir mimari ve üretken paradigma sunmaktadır. Organizasyonların karmaşıklıkta yol bulmalarını, birbirinden farklı sistemleri entegre etmelerini ve daha fazla stratejik uyum elde etmelerini sağlayan, doğrulanmış ve bütünsel bir meta-çerçeve sağlamaktadır. Anahtar Sözcükler: yönetim çerçeveleri, yönetim sistemi standartları, Entegre Yönetim Sistemi, bilgide birlik teorisi, ISO standartları, yönetim sistemi, sistem teorisi, sistem düşüncesi, Bütünlüklü Yapı Taşı Taksonomisi, Bütünlüklü Kök Yönetim Çerçevesi
