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Lookup NU author(s): Dr Xiaotian XieORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
The maritime industry is facing increasing challenges due to decarbonization requirements, trade disruptions, and geoeconomic fragmentation, such as International Maritime Organization (IMO) sets out clear framework to reach net zero emissions by 2050, Russia-Ukraine war disrupted maritime activities in the Black and Azov seas, and increased trade tensions between the United States and China. To enhance their sustainability, operational efficiency, and competitiveness, maritime organizations are therefore very keen to build big data analytics capability (BDAC). However, various barriers, mean that only a handful are able to do so. We adopt a mixed-method approach to analyze these barriers. Thematic analysis is used to identify five categories of barriers and 16 individual barriers based on empirical data collected from 26 maritime organizations. These are then prioritized using the analytic hierarchy process (AHP), followed by total interpretive structural modelling (TISM) to understand their interrelationships. Finally, cross-impact matrix multiplications applied to classification (MICMAC) is employed to differentiate the role of each barrier based on its driving and dependence power. This paper makes several theoretical contributions. First, China's hierarchical cultural value orientation encourages competition and obedience to rules, resulting in unwillingness to share knowledge, lack of coordination, and lack of error correction mechanisms. These cultural barriers hinder BDAC development. Second, organizational learning category barriers are found to be the most important in impeding BDAC development. This study also raises practitioners' awareness of the need to tackle cultural and organizational learning barriers.
Author(s): Zhao G, Xie X, Wang Y, Liu S, Jones P, Lopez C
Publication type: Article
Publication status: Published
Journal: Technological Forecasting and Social Change
Year: 2024
Volume: 203
Print publication date: 01/06/2024
Online publication date: 23/03/2024
Acceptance date: 16/03/2024
Date deposited: 02/10/2024
ISSN (print): 0040-1625
ISSN (electronic): 1873-5509
Publisher: Elsevier Inc.
URL: https://doi.org/10.1016/j.techfore.2024.123345
DOI: 10.1016/j.techfore.2024.123345
Data Access Statement: The authors confirm that the data supporting the findings of this study are available within the article [and/or] its supplementary materials.
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