Browsing by Author "Aydin, Mehmet Nafiz"

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Citation - WoS: 12
Citation - Scopus: 16
Design 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; Aydın, Mehmet Nafiz; Aydin, Mehmet Nafiz
It 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.
Citation - WoS: 2
Citation - Scopus: 6
Discovering Customer Purchase Patterns in Product Communities: An Empirical Study on Co-Purchase Behavior in an Online Marketplace
(Mdpi, 2021) Aydın, Mehmet Nafiz; 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.
Citation - WoS: 1
Citation - Scopus: 1
Innovation mentor community of practice: a social network analysis perspective
(Emerald Group Publishing Ltd, 2023) Aydın, Mehmet Nafiz; 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.
Management Frameworks and Management System Standards in the Context of Integration and Unification: a Review and Classification of Core Building Blocks for Consilience
(Mdpi, 2025) Gerek, Yalcin; Aydin, Mehmet Nafiz
Management frameworks (MFs) and management system standards (MSSs) are essential tools for improving organisational management practises. They inherently include a range of fundamental building blocks that facilitate the creation of structured management systems. However, these building blocks have not yet been holistically identified or unified into a consilient taxonomy. Addressing this research gap, this study conducts a comprehensive review of 415 academic papers and theses, 47 ISO MSSs, and 79 MFs sourced from scholarly databases and official publications. Utilising a novel heuristic methodology, this study integrates a literature review, clustering, text mining analytics, and an expert review to develop a Consilient Building Block Taxonomy (CBBT). This taxonomy categorises the foundational components of MFs and MSSs, presenting them as a structured framework that unifies these elements into a cohesive system. By providing a systematic classification, the CBBT serves as a foundation for the development of a Unified Singular Management System (USMS). The proposed taxonomy enhances operational coherence, strategic alignment, and efficiency by consolidating the core aspects of diverse management systems. This study concludes with insights into how the CBBT can be leveraged to achieve integration and unification in management practises, offering significant potential for both research and practical applications.
Citation - WoS: 1
Citation - Scopus: 1
Network analysis of innovation mentor community of practice
(Emerald Group Publishing Ltd, 2023) Aydın, Mehmet Nafiz; 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.