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Barrier analysis to improve big data analytics capability of the maritime industry: A mixed-method approach

Guoqing Zhao, Xiaotian Xie, Yi Wang, Shaofeng Liu, Paul Jones Orcid Logo, Carmen Lopez

Technological Forecasting and Social Change, Volume: 203, Start page: 123345

Swansea University Authors: Guoqing Zhao, Paul Jones Orcid Logo

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Abstract

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 activitie...

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Published in: Technological Forecasting and Social Change
ISSN: 0040-1625
Published: Elsevier BV 2024
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa65887
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Abstract: 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.
Keywords: Big data analytics capability (BDAC); Maritime industry; Barrier analysis; Analytic hierarchy process (AHP); Total interpretive structural modelling (TISM); Mixed methods
College: Faculty of Humanities and Social Sciences
Start Page: 123345