Yazar "Nikseresht, Ali" seçeneğine göre listele

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    Applications and emerging trends of blockchain technology in marketing to develop Industry 5.0 Businesses: A comprehensive survey and network analysis
    (Elsevier B.V., 2024) Nikseresht, Ali; Shokouhyar, Sajjad; Tirkolaee, Erfan Babaee; Pishva, Nima
    With the availability of enormous amounts of data come the difficulties of big data, privacy, and ransomware assaults, which result in Marketing fraud and spam. Blockchain offers an extensive array of possible applications in the Marketing field. Nevertheless, both Marketing research and practice exhibit a degree of hesitance toward using Blockchain technology and have not yet come around to completely understand and adopt the technology. Here, the aim is to examine the Blockchain concepts and their applications in Marketing through bibliometrics, network, and thematic analyses, which can provide several novel insights and perspectives into current research trends in this field by evaluating the most significant and cited research publications, keywords, institutions, authors' collaboration network, and finally countries that promote Industry 5.0 (I5.0) businesses. This study performs a detailed bibliometric and thematic-based Systematic Literature Review (SLR) on 124 of over 15000 research papers. Major outcomes include the identification of emerging themes such as the role of Blockchain in advertising, and dynamic pricing, as well as the need for further exploration of underdeveloped areas (e.g., consumer behavior and brand equity). The results contribute to theoretical and practical management elements and provide the groundwork for future study in this area. The overarching target of this research is to give a complete overview of applications and emerging trends of Blockchain technology in Marketing, thereby serving as a resource for future research topics for Marketing scholars and experts aiming to implement solutions based on Blockchain technology and algorithms to develop an I5.0 business. © 2024 Elsevier B.V.
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    An intelligent decision support system for warranty claims forecasting: Merits of social media and quality function deployment
    (Elsevier Science Inc, 2024) Nikseresht, Ali; Shokouhyar, Sajjad; Tirkolaee, Erfan Babaee; Nikookar, Ethan; Shokoohyar, Sina
    This work develops a novel approach based on Machine Learning (ML)-assisted Quality Function Deployment (QFD) to sift the gold from the stone. It includes Time-Varying Filter-based Empirical Mode Decomposition (TVFEMD), Deep Ensemble Random Vector Functional Link (DE-RVFL), and a Bayesian optimization algorithm for optimizing the shaped DE-RVFLTVF-EMD hyperparameters. This approach makes it possible for the proposed methods to be dynamic enough to deal with the data's volatility, complexity, uncertainty, and ambiguity. It is demonstrated that incorporating TVF-EMD to provide time-frequency analysis along DE-RVFL, and goal-oriented social media analytics boosts the performance of out-of-sample predictions statistically and compensates for the warranty data maturation effect. The proposed algorithm's Root Mean Square Error (RMSE) decreases by 23.37%-88.76% relative to other benchmark cutting-edge models. This study contributes significantly to the services management community. Using the proposed methodology, managers could create plans for warranty claims strategies that reduce inventory levels and waste while optimizing customer satisfaction, advocacy, and revenues. These merits provide incentives and support for policymakers to adopt advanced technologies, such as the ones developed and implemented in the current study, in warranty claims forecasting to improve accuracy and efficiency.