Sustainable future technology evaluation with digital transformation perspective in air cargo industry using IF ANP method

Title Sustainable future technology evaluation with digital transformation perspective in air cargo industry using IF ANP method
Author Havle, Celal Alpay, Büyüközkan, G.
Publication Date: 2023
Publication Place - Springer
Subject Air cargo industry, Digital transformation, IF ANP, Sustainability, Sustainable future technology evaluation
Type Document
Language English
Digital Yes
Manuscript No
Library: Özyeğin University
Library Asset ID 2367-3370
Record ID fbab03d7-e48c-4ce1-bf2e-3e4081caa8c3
Library Location Professional Flight Program
Date 2023
Notes Galatasaray Üniversitesi
Sample Text Sustainability is one of the most critical issues of recent years. Many companies that contribute to the global economy focus on sustainability studies more than ever. On the other hand, technological developments lead companies to digital transformation. This situation necessitated the adoption of a sustainability approach that includes digital transformation and digital technologies. Therefore, it is critical for companies embarking on a transformation journey to invest in technologies that support sustainability. Advances in technology and digital transformation have contributed to the growth of e-commerce volume globally. However, this has led to the growth and complexity of supply chain networks. One of the biggest players in this network structure is undoubtedly the air cargo industry. The air cargo industry plays a critical role in the sustainability of the global economy, employment, social development, and the environment. Therefore, this study proposes a new model for the evaluation of sustainable future technologies in the air cargo industry from the perspective of digital transformation. Multi-criteria decision-making (MCDM) approach was adopted in the study. The intuitionistic fuzzy analytical network process (IF ANP) is used to validate the proposed model in a real case conducted in the Turkish air cargo industry.
DOI 10.1007/978-3-031-39777-6_47
Cilt 759 LNNS
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Sustainable future technology evaluation with digital transformation perspective in air cargo industry using IF ANP method

Author Havle, Celal Alpay, Büyüközkan, G.
Publication Date 2023
Publication Place - Springer
Subject Air cargo industry, Digital transformation, IF ANP, Sustainability, Sustainable future technology evaluation
Type Document
Language English
Digital Yes
Manuscript No
Library Özyeğin University
Library Asset ID 2367-3370
Record ID fbab03d7-e48c-4ce1-bf2e-3e4081caa8c3
Library Location Professional Flight Program
Date 2023
Notes Galatasaray Üniversitesi
Sample Text Sustainability is one of the most critical issues of recent years. Many companies that contribute to the global economy focus on sustainability studies more than ever. On the other hand, technological developments lead companies to digital transformation. This situation necessitated the adoption of a sustainability approach that includes digital transformation and digital technologies. Therefore, it is critical for companies embarking on a transformation journey to invest in technologies that support sustainability. Advances in technology and digital transformation have contributed to the growth of e-commerce volume globally. However, this has led to the growth and complexity of supply chain networks. One of the biggest players in this network structure is undoubtedly the air cargo industry. The air cargo industry plays a critical role in the sustainability of the global economy, employment, social development, and the environment. Therefore, this study proposes a new model for the evaluation of sustainable future technologies in the air cargo industry from the perspective of digital transformation. Multi-criteria decision-making (MCDM) approach was adopted in the study. The intuitionistic fuzzy analytical network process (IF ANP) is used to validate the proposed model in a real case conducted in the Turkish air cargo industry.
DOI 10.1007/978-3-031-39777-6_47
Cilt 759 LNNS
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