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Öğe The effect of big data in transforming to learning organization a single-case study in IT sector(Emerald Group Publishing Ltd, 2022) Başak, Sena; Kılınç, İzzet; Unal, AslihanPurpose The purpose of this paper is to examine the contribution of big data in the transforming process of an IT firm to a learning organization. Design/methodology/approach The authors adopted a qualitative research approach to define and interpret the ideas and experiences of the IT firms' employees and to present them to the readers directly. For this purpose, they followed a single-case study design. They researched on a small and medium enterprise operating in the IT sector in Duzce province, Turkey. This paper used a semi-structured interview and document analysis as data collecting methods. In all, eight interviews were conducted with employees. Brochures and website of the organization were used as data sources for the document analysis. Findings As a result of in-depth interviews and document analysis, the authors formed five main themes that describe perception of big data and learning organization concepts, methods and practices adopted in transforming process, usage areas of big data in organization and how the sample organization uses big data as a learning organization. The findings of this paper show that the sample organization is a learning IT firm that has used big data in transforming to learning organization and in maintaining the learning culture. Research limitations/implications The findings contribute to literature as it is one of the first studies that examine the influence of big data on the transformation process of an IT firm to a learning organization. The findings reveal that IT firms benefit from the solutions of big data while learning. However, as the design of the research is single-case study, the findings may be specific to the sample organization. Future studies are required that examine the subject in different samples and by different research designs. Originality/value In literature, research on how IT firms' managers and employees use big data in organizational learning process is limited. The authors expect that this paper will shed light on future research that examines the effect of big data on the learning process of the organization.Öğe The feasibility of artificial intelligence performing as CEO: the vizier-shah theory(Emerald Group Publishing Ltd, 2021) Unal, Aslihan; Kilinc, IzzetPurpose This paper aims to examine the feasibility of artificial intelligence (AI) performing as chief executive officer (CEO) in organizations. Design/methodology/approach The authors followed an explorative research design - classic grounded theory methodology. The authors conducted face-to-face interviews with 27 participants that were selected according to theoretical sampling. The sample consisted of academics from the fields of AI, philosophy and management; experts and artists performing in the field of AI and professionals from the business world. Findings As a result of the grounded theory process The Vizier-Shah Theory emerged. The theory consisted of five theoretical categories: narrow AI, hard problems, debates, solutions and AI-CEO. The category AI as a CEO introduces four futuristic AI-CEO models. Originality/value This study introduces an original theory that explains the evolution process of narrow AI to AI-CEO. The theory handles the issue from an interdisciplinary perspective by following an exploratory research design - classic grounded theory and provides insights for future research.Öğe Green leadership: a scientific paradigm or a managerial fashion?(Emerald Group Publishing Ltd, 2026) Kilinc, Izzet; Ozcelik, Gulizar; Ertem, Mehmet; Unal, AslihanPurpose - This study critically explores the increasingly popular concept of green leadership within managerial and academic discourse. Although frequently presented as an ethically grounded leadership model aligned with sustainability goals, its maturity as a scientific paradigm remains questionable. The paper evaluates whether green leadership possesses the conceptual coherence, methodological structure, and problem-solving capacity necessary to qualify as a paradigm. Design/methodology/approach - Drawing on Thomas Kuhn's theory of paradigms, the study examines the conceptual foundations of green leadership and assesses its theoretical and methodological status. It further incorporates insights from Abrahamson's concept of management fashion and employs a critical management studies lens to explore the discursive construction of green leadership. Additionally, the analysis integrates Popper's falsifiability criterion and Lakatos's research programme model to evaluate the concept's scientific rigor. Findings - The analysis suggests that green leadership largely draws on transformational, ethical, and sustainable leadership approaches without offering an original theoretical foundation. From this perspective, it resembles a management fashion more than a paradigmatic framework. The study argues that the normative positivity surrounding green leadership may contribute to legitimizing existing organizational power structures. Furthermore, it identifies Western-centric development and universalist assumptions that challenge the concept's cultural adaptability in non-Western or developing contexts. Overall, the findings conclude that green leadership currently lacks sufficient explanatory power, conceptual coherence, and theoretical maturity to be considered a scientific paradigm. Originality/value - This study contributes by systematically evaluating green leadership through the lens of paradigm theory and the philosophy of science. It moves beyond normative and symbolic representations of green leadership and provides a critical and interdisciplinary perspective that highlights its conceptual limitations and cultural blind spots. The study emphasizes the need for further theoretically grounded, empirically robust, and culturally sensitive research to enhance the legitimacy and practical relevance of green leadership.Öğe A Novel and Robust LSTM Model for Customer Churn Analysis Using Deep, Machine Learning, and Ensemble Learning: A Telecommunications Case(Istanbul Univ, 2025) Basarslan, Muhammet Sinan; Unal, Aslihan; Kayaalp, FatihCustomer churn is an important issue in increasing both the long- and short-term revenues. If companies identify customers' churn behavior, they can prevent churn, ensure customer loyalty, and, in turn, gain better financial returns. The telecommunications sector is a customer-oriented sector that requires customer retention to survive in the market. In this sector, customer churn is observed at a high level. In recent years, artificial intelligence-based customer churn analysis has been widely used to predict customer churn behavior. In this study, a customer churn analysis was conducted using publicly shared Telco telecommunications data. Predictive models were constructed using machine learning (LR, KNN, SVM, DT, RF, ANN), ensemble learning (XGBoost, Majority Voting), and deep learning (LSTM) methods. In addition, a 3-layered LSTM model was proposed. Accuracy (Acc), F1-score (F1), Precision (Prec), and Recall (Rec) rates were used to evaluate the models. As a result, the novel3-layered LSTM model achieved 91.90% Acc, 91.49% Prec, 92.31% Rec, and 91.90% F1 values. The proposed model is competitive with the existing models.
