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Article
Publication date: 29 December 2021

Takuma Matsuda, Enna Hirata and Tomoya Kawasaki

Since the 2010s, market conditions for container shipping companies have been deteriorating owing to decreasing container cargo trade and increasing supply capacity. This study…

4277

Abstract

Purpose

Since the 2010s, market conditions for container shipping companies have been deteriorating owing to decreasing container cargo trade and increasing supply capacity. This study aims to contribute to the empirical literature on the container shipping industry market structure. Specifically, this study aims to investigate the extent of market competition.

Design/methodology/approach

This study analyzes the market structure and evaluates the market power of shipping companies through a non-structural test.

Findings

The H-statistic for the entire period of 2004–2018 was 0.37, which is significantly different from zero. This indicates the absence of monopoly pricing throughout the entire period. For the time-phased estimates, the H-statistic between 2004 and 2008 is 0.15, which is not significantly different from zero. On the other hand, the H-statistic from 2009 to 2018 was 0.40, which differs significantly from zero.

Originality/value

As the Far East Freight Conference had released tariffs and charge rates by item for container shipping routes, monopolistic pricing is said to have appeared until the European Union abolished the European Economic Community (No. 4056/86) in 2008, before the economic crisis. However, this study indicates that pricing in the container shipping industry has been distinctly non-monopolistic; further, competition seems to have intensified since 2008. Industry competitiveness is of interest not only to academics but also to practitioners, including policymakers, especially when considering competition policies.

Details

Maritime Business Review, vol. 7 no. 4
Type: Research Article
ISSN: 2397-3757

Keywords

Available. Content available
Article
Publication date: 14 December 2020

Enna Hirata, Maria Lambrou and Daisuke Watanabe

This paper aims to retrieve key components of blockchain applications in supply chain areas. It applies natural language processing methods to generate useful insights from…

5462

Abstract

Purpose

This paper aims to retrieve key components of blockchain applications in supply chain areas. It applies natural language processing methods to generate useful insights from academic literature.

Design/methodology/approach

It first applies a text mining method to retrieve information from scientific journal papers on the related topics. The text information is then analyzed through machine learning (ML) models to identify the important implications from the existing literature.

Findings

The research findings are three-fold. While challenges are of concern, the focus should be given to the design and implementation of blockchain in the supply chain field. Integration with internet of things is considered to be of higher importance. Blockchain plays a crucial role in food sustainability.

Research limitations/implications

The research findings offer insights for both policymakers and business managers on blockchain implementation in the supply chain.

Practical implications

This paper exemplifies the model as situated in the interface of human-based and machine-learned analysis, potentially offering an interesting and relevant avenue for blockchain and supply chain management researchers.

Originality/value

To the best of the knowledge, the research is the very first attempt to apply ML algorithms to analyzing the full contents of blockchain-related research, in the supply chain sector, thereby providing new insights and complementing existing literature.

Details

Maritime Business Review, vol. 6 no. 2
Type: Research Article
ISSN: 2397-3757

Keywords

Available. Content available
Article
Publication date: 25 October 2021

Enna Hirata and Takuma Matsuda

This research aims to uncover coronavirus disease 2019’s (COVID-19's) impact on shipping and logistics using Internet articles as the source.

5562

Abstract

Purpose

This research aims to uncover coronavirus disease 2019’s (COVID-19's) impact on shipping and logistics using Internet articles as the source.

Design/methodology/approach

This research applies web mining to collect information on COVID-19's impact on shipping and logistics from Internet articles. The information extracted is then analyzed through machine learning algorithms for useful insights.

Findings

The research results indicate that the recovery of the global supply chain in China could potentially drive the global supply chain to return to normalcy. In addition, researchers and policymakers should prioritize two aspects: (1) Ease of cross-border trade and logistics. Digitization of the supply chain and applying breakthrough technologies like blockchain and IoT are needed more than ever before. (2) Supply chain resilience. The high dependency of the global supply chain on China sounds like an alarm of supply chain resilience. It calls for a framework to increase global supply chain resilience that enables quick recovery from disruptions in the long term.

Originality/value

Differing from other studies taking the natural language processing (NLP) approach, this research uses Internet articles as the data source. The findings reveal significant components of COVID-19's impact on shipping and logistics, highlighting crucial agendas for scholars to research.

Details

Maritime Business Review, vol. 7 no. 4
Type: Research Article
ISSN: 2397-3757

Keywords

Available. Content available
Article
Publication date: 26 November 2020

Quazi Mohammed Habibus Sakalayen, Okan Duru and Enna Hirata

Bulk shipping mostly facilitates the smooth flow of raw materials around the globe. Regardless, forecasting a bulk shipbuilding orderbook is a seldom researched domain in the…

1356

Abstract

Purpose

Bulk shipping mostly facilitates the smooth flow of raw materials around the globe. Regardless, forecasting a bulk shipbuilding orderbook is a seldom researched domain in the academic arena. This study aims to pioneer an econophysics approach coupled with an autoregressive data analysis technique for bulk shipbuilding order forecasting.

Design/methodology/approach

By offering an innovative forecasting method, this study provides a comprehensive but straightforward econophysics approach to forecast new shipbuilding order of bulk carrier. The model has been evaluated through autoregressive integrated moving average analysis, and the outcome indicates a relatively stable good fit.

Findings

The outcomes of the econophysics model indicate a relatively stable good fit. Although relevant maritime data and its quality need to be improved, the flexibility in refining the predictive variables ensure the robustness of this econophysics-based forecasting model.

Originality/value

By offering an innovative forecasting method, this study provides a comprehensive but straightforward econophysics approach to forecast new shipbuilding order of bulk carrier. The research result helps shipping investors make decision in a capital-intensive and uncertainty-prone environment.

Details

Maritime Business Review, vol. 6 no. 3
Type: Research Article
ISSN: 2397-3757

Keywords

Available. Content available
494

Abstract

Details

Maritime Business Review, vol. 7 no. 4
Type: Research Article
ISSN: 2397-3757

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