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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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DOI (Published version): 10.1016/j.techfore.2024.123345

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
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URI: https://cronfa.swan.ac.uk/Record/cronfa65887
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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. 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spelling v2 65887 2024-03-24 Barrier analysis to improve big data analytics capability of the maritime industry: A mixed-method approach 2ff29aa347835abe2af6d98fa89064b4 Guoqing Zhao Guoqing Zhao true false 21e2660aaa102fe36fc981880dd9e082 0000-0003-0417-9143 Paul Jones Paul Jones true false 2024-03-24 BBU 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. Journal Article Technological Forecasting and Social Change 203 123345 Elsevier BV 0040-1625 Big data analytics capability (BDAC); Maritime industry; Barrier analysis; Analytic hierarchy process (AHP); Total interpretive structural modelling (TISM); Mixed methods 1 6 2024 2024-06-01 10.1016/j.techfore.2024.123345 COLLEGE NANME Business COLLEGE CODE BBU Swansea University SU Library paid the OA fee (TA Institutional Deal) 2024-03-25T09:55:14.6901244 2024-03-24T06:26:51.3717805 Faculty of Humanities and Social Sciences School of Management - Business Management Guoqing Zhao 1 Xiaotian Xie 2 Yi Wang 3 Shaofeng Liu 4 Paul Jones 0000-0003-0417-9143 5 Carmen Lopez 6 65887__29816__876c608bee0d4f5dbf35f04a3ba9ed9b.pdf 1-s2.0-S0040162524001410-main.pdf 2024-03-24T06:28:53.4795249 Output 1492314 application/pdf Version of Record true © 2024 The Authors. This is an open access article under the CC BY license. true eng http://creativecommons.org/licenses/by/4.0/
title Barrier analysis to improve big data analytics capability of the maritime industry: A mixed-method approach
spellingShingle Barrier analysis to improve big data analytics capability of the maritime industry: A mixed-method approach
Guoqing Zhao
Paul Jones
title_short Barrier analysis to improve big data analytics capability of the maritime industry: A mixed-method approach
title_full Barrier analysis to improve big data analytics capability of the maritime industry: A mixed-method approach
title_fullStr Barrier analysis to improve big data analytics capability of the maritime industry: A mixed-method approach
title_full_unstemmed Barrier analysis to improve big data analytics capability of the maritime industry: A mixed-method approach
title_sort Barrier analysis to improve big data analytics capability of the maritime industry: A mixed-method approach
author_id_str_mv 2ff29aa347835abe2af6d98fa89064b4
21e2660aaa102fe36fc981880dd9e082
author_id_fullname_str_mv 2ff29aa347835abe2af6d98fa89064b4_***_Guoqing Zhao
21e2660aaa102fe36fc981880dd9e082_***_Paul Jones
author Guoqing Zhao
Paul Jones
author2 Guoqing Zhao
Xiaotian Xie
Yi Wang
Shaofeng Liu
Paul Jones
Carmen Lopez
format Journal article
container_title Technological Forecasting and Social Change
container_volume 203
container_start_page 123345
publishDate 2024
institution Swansea University
issn 0040-1625
doi_str_mv 10.1016/j.techfore.2024.123345
publisher Elsevier BV
college_str Faculty of Humanities and Social Sciences
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hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
document_store_str 1
active_str 0
description 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.
published_date 2024-06-01T09:55:12Z
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