Browsing by Author "Aydin, Mehmet N."

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Citation Count: 1
Design Science Perspective on NFC Research: Review and Research Agenda
(Slovensko Drustvo Informatika, 2013) Aydin, Mehmet N.; Ozdenizci, Busra
Near Field Communication (NFC), as one of the emerging and promising technological developments, provides means to short range contactless communication for mobile phones and other devices alike. NFC has become an attractive design science research area for many academicians due to its exploding growth and its promising applications and related services. A better understanding of the current status of NFC research is necessary to maintain the advancement of knowledge in NFC research and to identify the gap between theory and practice. In this paper, we present a literature review on NFC. To facilitate the analysis of the literature, we propose a research framework and organize the NFC literature into four major categories (theory and development, applications and services, infrastructure, ecosystem). We contend that due to the nature of NFC (industry high stakes, multidisciplinary research, artifacts development), the design science research paradigm serves an appropriate ground to investigate an extent to which relevance and rigor is achieved. By employing the proposed research framework and design science perspective, we set up a research agenda (research directions and promising research questions) which may help practitioners and academics to achieve a substantial progress in NFC.
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.
Citation Count: 0
Understanding Virtual Onboarding Dynamics and Developer Turnover Intention in the Era of Pandemic
(Elsevier Science inc, 2024) Akdur, Gorkem; Aydin, Mehmet N.; Akdur, Gizdem
This 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.
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: 1
Validity Issues of Digital Trace Data for Platform as a Service: A Network Science Perspective
(Springer international Publishing Ag, 2018) Aydin, Mehmet N.; Kariniauskaite, Dzordana; Perdahci, N. Ziya
Data validity becomes a prominent research area in the context of data science driven research in the past years. In this study, we consider an application development on a cloud computing platform as a promising research area to examine digital trace data belonging to records of development activity undertaken. Trace data display such characteristics as found data that is not especially produced for research, event-based, and longitudinal, i.e., occurring over a period of time. Having these characteristics underlies many validity issues. We employ two application development trace data to articulate validity issues along with an iterative 4-phase research cycle. We demonstrate that when working with digital trace data, data validity issues must be addressed; otherwise it can lead to awry results of the research.