Barrier analysis to improve big data analytics capability of the maritime industry: a mixed-method approach
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Affiliation
Swansea UniversityUniversity of Surrey
University of Bedfordshire
University of Plymouth
University of Southampton
Issue Date
2024-03-23Subjects
Total interpretive structural modelling (TISM)mixed methods
barrier analysis
analytic hierarchy process (AHP)
Big data analytics capability (BDAC)
maritime industry
Subject Categories::N190 Business studies not elsewhere classified
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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.Citation
Zhao G, Xie X, Wang Y, Liu S, Jones P, Lopez C (2024) 'Barrier analysis to improve big data analytics capability of the maritime industry: a mixed-method approach', Technological Forecasting and Social Change, 203 (123345)Publisher
Elsevier Inc.Additional Links
https://www.sciencedirect.com/science/article/pii/S0040162524001410Type
ArticleLanguage
enISSN
0040-1625ae974a485f413a2113503eed53cd6c53
10.1016/j.techfore.2024.123345
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