Analysis of Key Factors and Correlations Influencing the Adoption of Autonomous Ships by Shipping Companies—A Study Integrating Revised DEMATEL-AHP with BOCR

Abstract

In response to achieving the United Nations Sustainable Development Goals (SDGs) of Climate Action (#13) and Life Below Water (#14), the promotion of autonomous shipping technologies has advanced from the experimental stage to specific regional implementation, presenting the maritime industry with rapid and significant changes and challenges. In the future era, where autonomous vessels dominate shipping, with automated operation systems taking the lead, how successfully shipping companies harness these new maritime transport modes will critically impact the safety, efficiency, and reliability of future vessel operations. With the emergence and development of autonomous vessels, it is crucial to effectively assess the importance and correlation of key factors influencing shipping companies’ adoption of autonomous ships. This study utilizes the Analytic Hierarchy Process (AHP) and Revised Decision Making and Trial Evaluation Laboratory (RDEMATEL) to survey senior managers in container and bulk shipping from Taiwan, China, Japan, and the European Union. Through a literature review on the benefits, opportunities, costs, and risks brought by autonomous shipping, this study aims to understand the critical factors important to shipping companies in adopting autonomous shipping, as well as the correlation between these influencing factors across different shipping sectors. The research findings indicate that “emergency response capability” is a critical factor influencing overall and bulk shipping in the adoption of autonomous vessels, while “incomplete regulations” are the primary factor influencing container shipping in the adoption of autonomous ships. Regarding the correlation of critical influencing factors, “vessel technology development” is the main influencing factor for overall, container, and bulk shipping; “operational performance enhancement” is the primary affected factor for overall and container shipping; and “enhancing personnel and vessel safety” is the main affected factor for bulk shipping. It is hoped that the results of this study can serve as a guide for shipping companies in understanding the benefits and opportunities to be emphasized when adopting autonomous shipping and assist in developing effective strategies to reduce costs and risks.

1. Introduction

Maritime Autonomous Surface Ships (MASS), as named by the International Maritime Organization (IMO), are also known as Autonomous Ships or Unmanned Surface Vehicles (USVs). These are surface vessels capable of independently carrying out missions at sea for extended periods and possess abilities such as controlling their position in the sea, conducting automated marine area surveys, navigation tracking, wireless data transmission, and the flexibility to be equipped with various sensors or measuring instruments.

Based on this framework, the classification of MASS is based on the level of unmanned or minimal human operation on the ship’s bridge. This includes:

• Level 1 (LV1): Crew onboard operating with automated procedures and decision support, known as “Autonomy Assisted Bridge” (AAB).
• Level 2 (LV2): Crew onboard with remote control capability, known as “Periodically Unmanned Bridge” (PUB).
• Level 3 (LV3): Remote control without onboard crew, referred to as “Periodically Unmanned Ship” (PUS).
• Level 4 (LV4): Ship operation system capable of autonomous decision-making and execution, known as “Continuously Unmanned Ship” (CUS) [1,2].

Recent experimental projects related to autonomous ships include four categories: cargo transport, maritime firefighting and search and rescue, maritime surveillance, and maritime research [3]. Huang et al. [4] further subdivided international applications of autonomous ships into six categories: government patrol, hydrological surveys, long-distance transportation, harbor cargo shuttle, harbor cleaning, and tourism. This indicates that the development of autonomous ships, due to the comprehensive benefits of reducing labor costs and enhancing personnel safety, has gradually expanded from early exploration of marine resources and national defense functions to the field of civil transportation, which has become a highly significant trend in the maritime industry.

Wang [1] stated that according to the market research consulting firm Verified Market Research, the global market size for autonomous ships is projected to reach USD 9.78 billion by 2026, with LV1 comprising the highest proportion, followed by LV2. Asia is expected to remain the largest market for autonomous ships, accounting for 60% of the global market, with China exhibiting the highest growth rate, which is estimated at 10% annually. However, fully autonomous vessels lack onboard crew facilities and do not require a manned Shore Control Center (SCC) onshore, making them more economically, socially, and environmentally feasible than other solutions for achieving the maximum benefits of automated shipping [5]. Recent experiments, however, have demonstrated that fully autonomous ships are not yet out of the research and development phase, making them unfeasible for long-distance voyages at the current stage.

Research by Munim et al. [6] indicates that fully autonomous ships are ultimately less favored by major stakeholders due to several factors. These ships are prohibitively expensive to build from a capital expenditure perspective, they face higher risks of navigating in adverse weather and complex geographic areas, and they are not very feasible regarding environmental considerations such as alternative fuels and existing legal regulations. In contrast, traditional vessels remain the preferred choice primarily because, although autonomous ships exist, their widespread commercial adoption is unlikely within the next 5–10 years. However, semi-autonomous ships (LV2) controlled remotely from shore control centers are ranked as the second priority for development and adoption by shipping companies. Given that autonomous shipping is an inevitable trend rather than an optional one, this study emphasizes practical application over theoretical and research-oriented aspects, focusing on evaluating semi-autonomous ships (LV2) remotely controlled from shore control centers as the subject of assessment by shipping companies.

This study gathers relevant Taiwanese and international research to understand the current development status of autonomous ships at home and abroad and the progress of international legislation. It also compiles key operational factors influencing shipping companies’ adoption of autonomous ships and conducts interviews and surveys with container shipping and bulk shipping companies from Taiwan, Japan, and the European Union. Ultimately, based on the perspective of shipping companies, this study aims to present research findings and recommendations. The goal is to identify and address critical obstacles and challenges encountered by shipping companies in adopting autonomous ships, as well as the correlation between key influencing factors, to prevent and respond to issues effectively. This study is structured as follows: Section 2 collects definitions related to autonomous ships and discusses their current applications in terms of benefits, opportunities, costs, and risks. Section 3 then explains the research methods used in this study, consolidates the key factors influencing shipping companies’ adoption of autonomous ships and their implications and constructs an evaluation framework diagram. Section 4 analyzes the importance and interrelationships of critical factors influencing shipping companies’ adoption of autonomous ships, discusses management implications, and finally concludes with recommendations.

2. Literature Review

This section reviews the benefits, opportunities, costs, and risks associated with autonomous ships in Taiwan and internationally. It also presents a comprehensive analysis.

2.1. Benefits and Opportunities

In terms of benefits and opportunities associated with autonomous ships, Şenol et al. [7] utilized the Analytic Hierarchy Process (AHP) to assess operational strategies related to autonomous ships. The advantages and opportunities identified include reduced accidents caused by crew language differences, increased cargo capacity, lowered labor costs, reduced port expenses, decreased fuel consumption, ship owners having direct control over vessels, reduced maritime accidents, and enhanced operational efficiency and environmental conservation. Regarding environmental conservation, research by Yara Birkeland indicates that autonomous ships not only reduce nitrogen oxide (NOx) and carbon dioxide (CO₂) emissions but also decrease onshore and port noise pollution [8]. Furthermore, autonomous ships achieve significant fuel cost savings compared to traditional vessels, with energy efficiency improvements of up to 74%, effectively reducing fuel expenditures [9].

Tsai et al. [10] suggest that as the development of autonomous ships will closely integrate shipping and port-related information and communication technologies and shipbuilding technologies, this will positively impact maritime safety, environmental protection, and operational efficiency. Kim and Mallam [11] further suggest that, in addition to the aforementioned benefits related to safety, reliability in ship operations, improved fuel consumption and operational efficiency, and reduction in human errors, autonomous ships can also address the current shortage of maritime industry personnel.

Recent research by Wang [1] suggests that the essential port equipment required for autonomous ships should include port infrastructure to support potential applications such as information services, advanced shore control centers, navigation aids, and automated berths, as well as improved Internet equipment for the ports, monitoring services for remote-controlled vessels, provision of autonomous tugs, mooring systems, floating buoys, charging facilities, and semi-automatic or fully automatic dock equipment. Hammad et al. [12], through a literature analysis, summarized the benefits and opportunities of autonomous ships. Dalaklis et al. [13] highlighted the critical importance of port digitization for autonomous vessels, proposing metrics such as Intelligent Traffic Flow, Intelligent Information Systems, Smart Port Infrastructure, and Hub Infrastructures as benchmarks for the “era of digitalization”. These indicators are intended to help shipping companies, freight forwarders, and rail operators adapt to emerging maritime technologies. Finally, improvements in land-based control centers and crew working conditions, better working environments, and reduction in tonne-km transported on road are additional potential benefits and opportunities that autonomous ships may bring [14].

2.2. Costs and Risks

As described above, autonomous ships bring numerous benefits and opportunities to shipping-related industries, but their costs and risks should not be overlooked. Critical shortcomings and risks include a lack of emergency response capabilities and measures, irregular maintenance planning, shortage of qualified crew and operators, high capital costs, reliability and security of information software, regulatory development and compliance issues, cybersecurity threats and piracy issues, the uncertainty of maritime insurance premiums, rescue costs during voyages and port investment, rights and obligations of stakeholders, and crisis resolution when encountering traditional vessels [7].

The uncertainty in autonomous ship insurance, limitations on the qualifications and numbers of shore control center manned staff, increased ship-to-shore communication, navigation, and power supply system, as well as the cumulative costs of maintaining network security, are all significant factors contributing to the rise in investment and operational costs of autonomous ships [15].

Chong [16] points out the challenges that autonomous ships face in the absence of unified maritime regulations and the need to strengthen navigational safety when operating in close proximity to traditional vessels. Communication between ships and the shore may be affected by reduced bandwidth capacity, radio interference, or loss/delay of communication data, potentially causing issues with collision-avoidance systems and increasing the risk of vessels getting too close or colliding. In the medium to long term, the International Maritime Organization faces significant challenges in unifying international standards and reducing regulatory disparities among countries concerning autonomous ships [17].

Tsai et al. [10] point out that it will not be cost-effective for autonomous ships to use robotics or remote control to replace ship crews for specific tasks such as cargo hold cleaning, minor repairs, cargo handling, theft prevention, damage mitigation, port operations, maritime rescue, pollution prevention after accidents, and management of refrigerated and hazardous goods. Further considerations must be made regarding maritime collision liability, societal acceptance, crew job security, opposition from seafarer unions, and the need to revise international and national regulations.

In terms of cybersecurity, Tusher et al. [18] highlight that the increased network connections of autonomous ships compared to traditional vessels pose increased complexity and security challenges in the integration of information with the shipping industry’s existing data from propulsion control systems, port operations, shore control centers, and shore-based management offices. Factors such as government regulations, the financial health of shipping companies, space requirements for port operations, and public opposition and complaints also contribute to the risks associated with shipping companies adopting autonomous ship operations [11,14].

Through a literature review, Hammad et al. [12] identified several drawbacks and threats associated with autonomous ships. These include insufficient regulatory standards, susceptibility to cyberattacks, reliability of ship-to-shore satellite communications, competency and suitability of crew, high initial investment costs, incompatibility with existing port infrastructure, anticipation of future crew shortages, and problems in preventing stowaways. The introduction of autonomous shipping (AS) into the maritime industry is driving a substantial shift from traditional shipping practices to a digitalized operational model. However, this innovation is not without risks, including challenges such as navigational failures, piracy, cyberattacks, and the varying state of infrastructural development across regions [19].

Research by Park et al. [20] found that support from top management, flexibility in financial resources, and intense competition significantly influence the willingness to adopt autonomous ships. Considering these findings, Li and Yuen [21] suggest that addressing issues such as strengthening regulatory standards for autonomous ships, improving insurance systems, implementing government subsidy policies, and enhancing crew training could increase the willingness of relevant stakeholders to adopt autonomous ship technologies.

Regarding research methodology, AHP and RDEMATEL have proven to be more effective than other MCDM methods when calculating the weights of constructs and criteria and clarifying the relationships between criteria. Numerous recent studies have combined AHP with other research methods in the field of shipping research, such as assessing the operational strategies of Romanian shipyards [22].

On the other hand, RDEMATEL, a revised version of DEMATEL introduced in 2013, has been less utilized in shipping industry research, with only Ho and Lee [23] applying it to a study on Taiwan shipyards’ installation of desulfurization equipment technology and services. Furthermore, no research is currently combining AHP, RDEMATEL, and BOCR in the maritime field. The closest example is a study by Wadjdi and Hayuningtyas [24] that applied a combination of AHP, DEMATEL, and BOCR to examine Indonesia’s government policies on opening domestic mining industries to foreign investment.

The rapid development of autonomous shipping technology is transitioning from the experimental to the implementation phase, presenting significant benefits and opportunities for shipping companies. However, this transition also introduces numerous negative impacts of increased costs and risks. The topic of autonomous ships highlights a potential future for the maritime business sector. However, previous studies have not distinguished between different types of shipping companies or comprehensively analyzed the benefits, opportunities, costs, and risks that autonomous ships present to the industry. This gap makes it challenging to develop holistic operational strategies for shipping companies.

In light of these considerations, this study employs the AHP–RDEMATEL method in conjunction with the BOCR framework to construct an analysis of the importance and interrelationships of key influencing factors for shipping companies in adopting autonomous ships. This study considers the perspectives of container, bulk, Taiwanese, international, near-sea, and deep-sea shipping companies to ensure comprehensive and well-rounded results.

3. Methodology and Evaluation Framework

Emerging automation models in the maritime industry present various positive and negative challenges. Below is an explanation of the research methodology and the framework for evaluating key influencing factors, including the evaluation constructs and criteria and their connotations.

3.1. Analytic Hierarchy Process (AHP)

In today’s rapidly evolving maritime innovation industry, shipping companies face complex and dynamic decision-making challenges. The AHP simplifies these challenges by breaking down intricate decision problems into a hierarchical structure. By employing pairwise comparisons, AHP assesses the relative importance of various criteria, providing a clear representation of decision-makers’ logic and quantifying the significance of influencing factors. This offers shipping companies an objective reference for their decision-making processes. AHP generally follows three main steps: structuring, measuring, and synthesizing. Structuring involves defining the alternatives, criteria, and objectives of the decision problem. Measuring entails assessing the criteria and assigning weights to each construct and criterion. Synthesizing combines the weights to derive the overall weights of each criterion or alternative [25]. The algorithmic steps are as follows: Step 1: create a hierarchical structure; Step 2: calculate the weights of hierarchical decision factors; and Step 3: calculation of hierarchical weights (please refer to the Appendix A for the calculation process).

According to Khan et al. [26], due to AHP’s solid mathematical foundation and the steps involved in estimating weights through building hierarchical structures, pairwise comparison matrices, eigenvalues, and eigenvectors, AHP has made a significant contribution to practical research and development, becoming the most widely used MCDM method for several decades. Other than AHP, the BOCR framework can also be applied in various other research methods, including DEMATEL and RDEMATEL.

In the context of the application of AHP to autonomous vessel research, Karatuğ et al. [27] applied AHP and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) to determine the optimal engine maintenance strategy for autonomous ships with varying levels of autonomy. They examined three research dimensions—economic, managerial, and technological—considering factors such as investment costs, operating costs, crew training expenses, crew safety, vessel fuel consumption and emissions, adaptability of existing and new vessels, spare parts availability, system reliability, system risks, system lifespan, infrastructure suitability, and maintenance efficiency.

3.2. Revised Decision-Making Laboratory Analysis Method (RDEMATEL)

Decision Making Trial and Evaluation Laboratory (DEMATEL) assesses the degree of influence between factors, calculates the causal relationships and strength among all factors using matrices and related mathematical theories, and evaluates direct, indirect, and comprehensive influences among factors through comparative analysis of interrelationships. This method aids in understanding the essence of problems and contributes to devising strategies for addressing related issues. The RDEMATEL methodology excels in addressing complex systems by directly comparing the interrelationships among variables. Through matrix computations, it identifies the direct and indirect causal relationships and the degree of influence between variables. These relationships are visualized in causal diagrams, which help pinpoint core issues and guide improvements within intricate systems.

However, the initial direct relation matrix in traditional DEMATEL does not converge effectively. This issue can be addressed by the Revised Decision Making Trial and Evaluation Laboratory (RDEMATEL) method [28]. Overall, for RDEMATEL and DEMATEL, the only difference lies in the calculation of direct/indirect and total relation matrices. The original DEMATEL assumes that its initial direct relation matrix $X^m$ will converge to zero. However, the assumption $\underset{m\to \infty }{\lim }{X}^m={\left[0\right]}_{n\times n}$ is not always valid. Therefore, by adding a small positive value $\mathsf{\epsilon }$ to the above formula, the initial direct relation matrix of the original DEMATEL can be made to converge to zero. The RDEMATEL computation steps are as follows: Step 1: define factors and determine relationships; Step 2: generate a direct-relation matrix $A$; Step 3: calculate the normalized direct relationship; Step 4: calculate the total relational matrix of direct/indirect effects; and Step 5: draw a cause-and-effect relationship (please refer to the Appendix A for the calculation process).

The integration of AHP and RDEMATEL leverages the strengths of both methods, adding substantial value to organizational decision-making processes. First, AHP aids decision-makers in clarifying objectives and identifying key factors, laying a solid foundation for subsequent RDEMATEL analysis. Next, RDEMATEL simulates various scenarios, evaluates the outcomes of different decision alternatives, and provides comprehensive support for final decision-making.

Ho et al. [29] recognized the lack of prior research applying RDEMATEL in assessing key influencing factors for Ocean Freight Forwarders (OFFs) in selecting Container Shipping Lines (CSLs). They employed the marketing 4C framework to construct a framework for assessing factors influencing the selection of CSL services. Similarly, Hsu and Ho [30] utilized the 4C framework in combination with the Fuzzy Delphi Method (FDM) and RDEMATEL to examine the appropriateness and correlation of key influencing factors for high-tech industry shippers when selecting Container Shipping Lines. Their study effectively provided recommendations for container shipping companies in formulating future operational strategies.

3.3. Research Framework

Through a comprehensive literature review, this study aims to identify the benefits, opportunities, costs, and risks that autonomous ships will bring to relevant stakeholders. Therefore, the Benefits–Opportunities–Costs–Risks (BOCR) framework proposed by Saaty [31] is adopted. This framework has been widely applied in various fields, including business, economics, engineering, management, and social sciences. The sources of journal publications are broad and mainly include engineering, computer science, business, decision sciences, energy, environmental sciences, and mathematics, and the methodological contributions of the BOCR framework have also been widely recognized. However, most BOCR work has been conducted in the aforementioned fields. Since this study is focused on practical maritime research, adopting the BOCR framework, which has extensive, practical applications, can provide a deeper understanding of the current benefits and costs associated with adopting autonomous ships by shipping companies, as well as the potential future opportunities and risks. This approach also aligns well with the evaluation criteria identified in this study.

In selecting the optimal research dimensions, this study considered the universal decision-making goal of maximizing benefits and opportunities while minimizing costs and risks. The BOCR model (Benefits, Opportunities, Costs, and Risks) addresses both positive and negative impacts, offering a clear prioritization of criteria under each dimension when integrated with AHP. This allows shipping companies to identify significant benefits and opportunities while anticipating potential costs and risks, enabling a proactive strategy formulation for future operations. Given its advantages over other models, the BOCR framework has been widely applied across various fields, making it particularly suited for this research.

In this study, some important words are defined as follows:

• Benefits: direct advantages that can be obtained after making a decision, such as improved performance and safety and reduced operational costs.
• Opportunities: potential future benefits that can be obtained after making a decision, such as indirect benefits in shipbuilding technology, port equipment updates, and information service improvements driven by autonomous ship development, as well as environmental benefits.
• Costs: the pain and disappointment caused by the decision, such as the increased estimated operating costs caused by the direct training costs, the initial significant investment, and the increase in insurance costs.
• Risks: the potential pain and disappointment that may arise after a decision, encompassing regulatory authority, care obligations, emergency response measures, and the willingness of related businesses to invest.

Regarding the application of BOCR to the analysis of the importance and correlation of key influencing factors in shipping companies’ introduction of autonomous ships, its evaluation criteria and connotations of the evaluation criteria included in the benefits, opportunities, costs, and risks are shown in

Table 1. Evaluation criteria for key influencing factors in shipping companies’ adoption of autonomous ship operations.

Factors	Criteria	Description	Source
(B)	(B1) Enhance Operational Efficiency	Improve remote flow control and monitoring to enhance supply chain efficiency. Increase port operation efficiency. Increase shipping company revenue.	[7,10,12,14]
	(B2) Enhance Personal and Ship Safety	Suitable for performing tasks in dangerous areas or unseaworthy adverse weather and sea conditions. Avoid accidents caused by human factors such as fatigue, insufficient training, misjudgment, and work attitude. Pirates are less able to take hostages. Reduce the chance of ship collisions. Lower the accident rate and mortality rate of crew members due to language differences.	[4,7,8,10,11,12,14,32]
	(B3) Reduce Operating Cost Data	Reduce crew costs. Address crew shortage issues. Reduce crew living space and facilities to increase cargo capacity. Lower fuel costs. Reduce port fees. Minimize crew replacement costs.	[4,7,9,10,12,15,32]
	(B4) Stable and Reliable Schedules	More accurate departure and arrival times. High system availability and reliability.	[11,16]
(O)	(O1) Environmental Protection Factors	Reduce fuel consumption and pollution. Minimize noise pollution from maritime and land transportation. Reduce heavy reliance on oil and natural gas.	[6,7,8,10,12,14,15,21,32]
	(O2) Ship Technology Development	Including autonomous control technologies for safe navigation in and out of ports, such as: Ship navigation systems. Visual systems. Hydrographic measurement systems. Power systems. Energy management systems. Communication systems. Sensors for ensuring safe navigation/avoiding collisions with smaller vessels lacking an Automatic Identification System (AIS), such as: Optical cameras. Sonar. Navigation radar. Collision avoidance radar. Lidar. Other task-specific sensors. Other: Development of human–machine interfaces and automation systems usable on ships and control centers, serving as communication tools with traditional vessels. Integration of ship design, decision-making, and operational systems.	[1,3,4,7,10,15,16,17,19,33]
	(O3) Port Infrastructure	Establish advanced shore control centers. ⚬ Information services, ship-to-shore communication systems, and radio interference prevention equipment. ⚬ Navigation aids and automated terminals. Procure autonomous tugboats, mooring systems, floating buoys, and automatic charging facilities. Potential expansion of port area may be required.	[1,3,12,13,14,15,19]
	(O4) Port Information Services	Establish continuous and intermittent remote monitoring systems. Enhance port Internet infrastructure. Providing services for autonomous vessels: ⚬ Route planning. ⚬ Communication. ⚬ Scheduling port entry and exit times. ⚬ Obtaining permits. ⚬ Arranging berths. ⚬ Providing pilotage and tugboat services. Address message delay issues. Integrate operational management and administrative systems. Prevent communication delays or information loss for ships.	[1,4,6,13,17,18]
(C)	(C1) Education and Training Costs	Crew recruitment and training. Direct, comprehensive response to the integration of human and non-human agents. Learning to collaborate with non-human intelligent agents. Innovation and creativity to adapt to evolving autonomous ship technology. Regime for convergence of company crew training and university education.	[2,10,11,16,33]
	(C2) Initial Investment Costs	Initial shipbuilding, port investment, and operating costs are high.	[6,7,12,21]
	(C3) Difficulties in Estimating Insurance Premiums	Since ship insurance rates depend on vessel safety, and the difficulty of assessing the safety and risks of autonomous ships may lead to significant uncertainties in estimating both operational and insurance costs for shipping companies.	[7,15,21]
	(C4) Automated Cargo Hold Cleaning Costs	Implementation of robotic or automated cargo hold cleaning systems involves significant upfront costs.	[10]
(R)	(R1) Incomplete Regulatory Framework	Incomplete ship inspection standards and navigational regulations instill fears that these issues may affect progress in automated ship design, manufacture, adoption, and use. Progress of revision of international conventions and domestic laws. Inconsistency of regulatory systems between countries. Liability and responsibility for property, cargo, and human life.	[1,3,7,10,11,12,16,17,20,21]
	(R2) Emergency Response Capability	Repairing and maintaining ocean-going vessels during voyages requires manpower. Without the crew, there is no manpower for an emergency response to accidents or severe weather. Must develop plans and protocols for temporary or regular maintenance. Cargo salvage costs. Stowaway prevention.	[6,7,10,11,12,16,17]
	(R3) Cargo Handling Responsibility	Equipment maintenance. Minor repairs. Cargo theft prevention and security. Mitigation of damage escalation. Maritime distress assistance. Pollution prevention after accidents. Security and prevention of pirate attacks. Resilience against cyber attacks. Control of refrigerated container power supply, temperature, and humidity.	[10,16,19]
	(R4) Level of Social Acceptance	Shipowner and shipyard technical and financial privacy. Seafarers’ union opposition. Conservative investment attitudes of port operators and shipping companies. Employment rates and revenue issues. Complaints from civil organizations. National policy support. Company size. Support from senior management in the organization. Company financial strength. Willingness to use autonomous ships. Degree of shipping competition. Allocation of maritime and shore control rights.	[3,7,10,14,15,20]

.

As can be seen in Table 1:

• Benefits include four evaluation criteria: enhancing operational performance, improving crew and vessel safety, reducing operating costs, and ensuring reliable scheduling.
• Opportunities encompass four evaluation criteria: environmental conservation factors, ship technology development, port infrastructure, and port information services.
• Costs involve four evaluation criteria: education and training expenses, investment and development costs, difficulty estimating premiums, and automated cleaning costs.
• Risks consist of four evaluation criteria: incomplete regulations, emergency response capabilities, cargo handling obligations, and societal acceptance level.

Figure 1 illustrates the framework for analyzing the factors and correlations affecting shipping companies’ implementation of autonomous ship operations.

4. Empirical Analysis

In this section, we conduct an empirical analysis based on valid questionnaires collected from shipping industry experts, reflecting their work experience and perceptions. Using Excel, we analyze the key influencing factors and correlations between these factors for shipping companies overall and across different attributes when adopting autonomous ship technology.

4.1. Survey Analysis

This study employed an expert questionnaire approach for data collection. The questionnaires were distributed via email, and follow-up communication was conducted through email to ensure that respondents fully understood the research questions and objectives. In-depth interviews were conducted with respondents who suggested modifications to the criteria, aiming to understand the reasons behind these modifications. As Aly and Vrana [34] emphasized, factors such as professional knowledge, work experience, and relevance to the research topic are crucial considerations in selecting experts for multi-criteria decision-making.

Furthermore, to ensure the practicality of the research results and the representativeness of the study subjects, this study conducted a questionnaire survey targeting senior managers—General Managers, Deputy General Managers, and Managers—with over 15 years of experience in planning departments at companies including A.P. Moller–Maersk, China COSCO Shipping, Courage Marine, Evergreen Marine, Hapag-Lloyd, Kuang Ming Shipping, Mediterranean Shipping, Mitsui O.S.K. Lines, Nippon Yusen Kaisha Lines, Ocean Network Express, U-Ming Marine, Wan Hai Lines, Wisdom Marine Lines, and Yang Ming Lines.

In this study, 35 questionnaires were distributed, with all 35 being returned. After excluding those with insufficient consistency, there were 22 valid questionnaires, resulting in an effective response rate of 62.86%. Among the valid responses, the breakdown by shipping category was as follows: five Taiwanese container shipping companies, six foreign container shipping companies, four deep-sea container shipping companies, seven near-sea container shipping companies; three Taiwanese bulk shipping companies, eight foreign bulk shipping companies, nine deep-sea bulk shipping companies, and two near-sea bulk shipping companies. Regarding the nationality of the shipping companies represented in the survey, the respondents included major shipping companies from Taiwan, China, Japan, and the European Union.

4.2. Importance Analysis of Critical Influencing Factor by Subgroups

4.2.1. Overall Importance Analysis

The ranking of the importance of key influencing factors for shipping companies overall and for container and bulk shippers’ adoption of autonomous ship operations is presented in

Table 2. Importance ranking of key influencing factors for overall shippers and container and bulk shipping for shipping companies’ adoption of autonomous ship operations.

Evaluation Factors	Weight (Ranking)			Evaluation Criteria	Weight			Weight (Ranking)
	Overall	Container	Bulk		Overall	Container	Bulk	Overall	Container	Bulk
B	0.2318	0.2615	0.2254	Enhancing Operational Performance	0.2449	0.2407	0.2544	0.0568	0.0629	0.0573
				Improving Personal and ship Safety	0.2943	0.2970	0.2476	0.0682	0.0776 (3)	0.0558
				Reducing Operating Costs	0.2484	0.2471	0.2513	0.0576	0.0646	0.0566
				Ensuring Reliable Scheduling	0.2124	0.2152	0.2467	0.0492	0.0563	0.0556
O	0.1718	0.1999	0.2185	Environmental Conservation Factors	0.2137	0.2615	0.2036	0.0367	0.0523	0.0445
				Ship Technology Development	0.2851	0.1999	0.2834	0.0490	0.0400	0.0619
				Port Infrastructure	0.2497	0.2326	0.2494	0.0429	0.0465	0.0545
				Port Information Services	0.2515	0.3060	0.2636	0.0432	0.0612	0.0576
C	0.2307	0.2326	0.2522	Education and Training Expenses	0.2308	0.2625	0.2197	0.0532	0.0611	0.0554
				Investment and Development Costs	0.3182	0.2963	0.2684	0.0734	0.0689	0.0677
				Difficulties in Estimating Premiums	0.2331	0.2137	0.2725	0.0538	0.0497	0.0687
				Automated Cleaning Costs	0.2179	0.2275	0.2394	0.0503	0.0529	0.0604
R	0.3657 (1)	0.3060 (1)	0.3039 (1)	Incomplete Regulations	0.2880	0.2839	0.2560	0.1053 (2)	0.0869 (1)	0.0778 (2)
				Emergency Response Capabilities	0.3007	0.2717	0.2793	0.1099 (1)	0.0831 (2)	0.0849 (1)
				Cargo Handling Obligations	0.2353	0.2385	0.2490	0.0861 (3)	0.0730	0.0757 (3)
				Societal Acceptance Level	0.1760	0.2059	0.2157	0.0644	0.0630	0.0656

.

Table 2 shows that the most important evaluation factor across the overall shipping industry, as well as container and bulk shipping individually, is “risk” (36.57%, 30.60%, and 30.39%, respectively).

The remaining factors follow a similar order across container shipping and the shipping industry overall, ranked as follows: “benefits” (26.15%, 23.18%), “costs” (23.26%, 23.07%), and “opportunities” (19.99%, 17.18%). On the other hand, for bulk shippers, following “risk”, the order shifts to “costs” (25.22%), “benefits” (22.54%), and “opportunities” (21.85%).

Regarding the evaluation criteria, the top five key influencing factors for the overall industry, ranked in order of importance, are “emergency response capability” (10.99%), followed by “incomplete regulations” (10.53%), “cargo handling obligations” (8.61%), “investment and development costs” (7.34%), and “improving personal and ship safety” (6.82%). These five factors collectively account for 44.29% of the importance ranking.

For container shipping, the top five factors are “incomplete regulations” (8.69%), “emergency response capability” (8.31%), “improving personal and ship safety” (7.76%), “cargo handling obligations” (7.30%), and “investment and development costs” (6.89%).

For bulk shipping, the top five factors are ranked as “emergency response capability” (8.49%), “incomplete regulations” (7.78%), “cargo handling obligations” (7.57%), “difficulties in estimating premiums” (6.87%), and “investment and development costs” (6.77%).

Among all the factors, “emergency response capability”, “incomplete regulations”, “cargo handling obligations”, and “investment and development costs” are identified as the key factors influencing the adoption of autonomous ships by container shipping, bulk shipping, and the shipping industry as a whole.

Regarding benefits, “improving personal and ship safety” and “enhancing operational performance” are considered the most important. Regarding opportunities, “ship technology development” and “port information services” are the most important. For costs, “investment and development costs” and “difficulties in estimating premiums” are deemed most significant. In the risk dimension, “emergency response capability” and “incomplete regulations” are the top concerns.

However, when consolidating all evaluation factors and criteria, most relatively, more critical factors are within the “risk” dimension. This indicates that shipping companies are especially concerned about the potential risks associated with adopting autonomous ships.

4.2.2. Subgroup Importance Analysis

The subgroup importance ranking of the key influencing factors for adopting autonomous ships in container and bulk shipping is shown in

Table 3. Subgroup importance ranking of key influencing factors for the adoption of autonomous ships in container and bulk shipping.

Evaluation Criteria	Weighting				Weighting
	Container Shipping				Bulk Shipping
	Near-Sea	Deep-Sea	Taiwan	Foreign	Near-Sea	Deep-Sea	Taiwan	Foreign
Enhancing Operational Performance	0.0543	0.0727	0.0672	0.0587	0.0589	0.0610	0.0553	0.0648
Improving Personal and Ship Safety	0.0641	0.0759	0.0742	0.0658	0.0639	0.0547	0.0611	0.0571
Reducing Operating Costs	0.0616	0.0658	0.0654	0.0621	0.0580	0.0612	0.0563	0.0629
Ensuring Reliable Scheduling	0.0533	0.0662	0.0556	0.0636	0.0610	0.0571	0.0583	0.0598
Environmental Conservation Factors	0.0550	0.0596	0.0531	0.0619	0.0544	0.0514	0.0499	0.0560
Ship Technology Development	0.0549	0.0572	0.0539	0.0584	0.0624	0.0625	0.0642	0.0610
Port Infrastructure	0.0533	0.0586	0.0560	0.0559	0.0599	0.0574	0.0588	0.0582
Port Information Services	0.0537	0.0555	0.0534	0.0560	0.0620	0.0584	0.0601	0.0602
Education and Training Expenses	0.0680	0.0565	0.0605	0.0636	0.0553	0.0628	0.0578	0.0601
Investment and Development Costs	0.0716	0.0605	0.0645	0.0674	0.0653	0.0650	0.0682	0.0622
Difficulties in Estimating Premiums	0.0621	0.0503	0.0517	0.0606	0.0643	0.0670	0.0672	0.0642
Automated Cleaning Costs	0.0628	0.0529	0.0562	0.0593	0.0549	0.0689	0.0574	0.0660
Incomplete Regulations	0.0809	0.0672	0.0765	0.0715	0.0737	0.0663	0.0752	0.0650
Emergency Response Capabilities	0.0684	0.0761	0.0770	0.0682	0.0751	0.0710	0.0756	0.0708
Cargo Handling Obligations	0.0692	0.0660	0.0716	0.0642	0.0681	0.0697	0.0683	0.0696
Societal Acceptance Level	0.0668	0.0590	0.0632	0.0628	0.0628	0.0656	0.0663	0.0621

.

From Table 3, we can see the key influencing factors for the adoption of autonomous ships in container shipping companies, analyzed by different subgroups, are as follows:

• Near-Sea Container Shipping Companies:
  Incomplete Regulations (8.09%)
  Investment and Development Costs (7.16%)
  Cargo Handling Obligations (6.92%)
• Deep-Sea Container Shipping Companies:
  Emergency Response Capability (7.61%)
  Improving Personal and Ship Safety (7.59%)
  Enhancing Operational Performance (7.27%)
• Taiwanese Container Shipping Companies:
  Emergency Response Capability (7.70%)
  Incomplete Regulations (7.65%)
  Improving Personal and Ship Safety (7.42%)
• Foreign Container Shipping Companies:
  Incomplete Regulations (7.15%)
  Emergency Response Capability (6.82%)
  Investment and Development Costs (6.74%)

In terms of the subgroup analysis for bulk shipping, the top three key factors influencing the adoption of autonomous ships are as follows:

• Near-Sea Bulk Shipping Companies:
  Emergency Response Capability (7.51%)
  Incomplete Regulations (7.37%)
  Cargo Handling Obligations (6.81%)
• Deep-Sea Bulk Shipping Companies:
  Emergency Response Capability (7.10%)
  Cargo Handling Obligations (6.97%)
  Automated Cleaning Costs (6.89%)
• Taiwanese Bulk Shipping Companies:
  Emergency Response Capability (7.56%)
  Incomplete Regulations (7.52%)
  Cargo Handling Obligations (6.83%)
• Foreign Bulk Shipping Companies:
  Emergency Response Capability (7.08%)
  Cargo Handling Obligations (6.96%)
  Automated Cleaning Costs (6.60%)

4.3. Key Influencing Factors’ Correlation Analysis

4.3.1. Container, Bulk, and Overall Industry Correlation Analysis

This study uses the Revised Decision-Making Trial and Evaluation Laboratory (RDEMATEL) method to understand the correlation between key factors influencing the adoption of autonomous ship operations by shipping companies as a whole and by different types of companies. This method evaluates the interrelationships among factors. To retain more decisive factors and avoid overly complex causal diagrams, threshold values of 0.24, 0.31, and 0.27 were used for the overall, container, and bulk shipping analyses, respectively.

Table 4. Numerical causal relationships of key factors influencing the introduction of autonomous ships for the overall, container, and bulk shipping industry.

Influencing Factor	${\mathit{D}}_{\mathit{k}}$			${\mathit{R}}_{\mathit{k}}$			${\mathit{D}}_{\mathit{k}}+{\mathit{R}}_{\mathit{k}}$			${\mathit{D}}_{\mathit{k}}-{\mathit{R}}_{\mathit{k}}$
	Overall	Container	Bulk	Overall	Container	Bulk	Overall	Container	Bulk	Overall	Container	Bulk
(B1) Enhancing Operational Performance	0.2382	0	0.5649	0.5085	0.9741	0.5741	0.7467	0.9741	1.1390	−0.3267	−0.9741	−0.0092
(B2) Improving Personal and Ship Safety	0	-	0	1.0731	-	0.8703	1.0731	-	0.8703	−1.0731	-	−0.8703
(O2) Ship Technology Development	0.5342	0.6414	0.9112	0	0	0	0.5342	0.6414	0.9112	0.5342	0.6414	0.9112
(O3) Port Infrastructure	0.2417	0.3315	-	0	0	-	0.2417	0.3315	-	0.2417	0.3315	-
(C1) Education and Training Expenses	0.2721	-	-	0	-	-	0.2721	-	-	0.2721	-	-
(C2) Investment and Development Costs	0	0.3193	0	0.2745	0.3181	0.5975	0.2745	0.6374	0.2975	−0.2745	0.0012	−0.5975
(R2) Emergency Response Capabilities	0.5336	-	0.5658	0	-	0	0.5336	-	0.5658	0.5336	-	0.5658

shows the values of the causal relationships for key factors influencing shipping companies’ adoption of autonomous ship operations.

Table 4 reveals that “ship technology development” is the primary influencing factor overall and also for container and bulk shipping. “Enhancing operational performance” is the main affected factor overall and for container shipping, while “improving personal and ship safety” is the primary affected factor for bulk shipping. To understand the causal relationships between the main influencing and affected factors overall and for container and bulk shipping, refer to Figure 2, Figure 3 and Figure 4.

In the assessment of shipping companies overall, “ship technology development” and “emergency response capability” are factors that influence “enhancing operational performance”. Figure 2 shows the causal relationships of key influencing factors for shipping companies’ introduction of autonomous ships.

In the overall container shipping sector, “port infrastructure”, “ship technology development”, and “investment and development costs” are factors that influence “enhancing operational performance” for container shipping companies. Figure 3 shows the causal relationships of key influencing factors for container shipping companies’ introduction of autonomous ships.

In the overall bulk shipping sector, “ship technology development”, “emergency response capability”, and “enhancing operational performance” are factors influencing the “enhancement of personal and ship safety”. Figure 4 shows the causal relationships of key influencing factors for the introduction of autonomous ships by bulk shipping companies.

4.3.2. Subgroup Causal Relationship Analysis

In the subgroup analysis, the threshold values for the causal relationship analysis of key influencing factors for introducing autonomous ships by container shipping companies are 0.39 for Taiwanese companies, 0.18 for foreign companies, 0.38 for near-sea shipping, and 0.28 for deep-sea shipping.

The threshold values for bulk shipping companies are 0.38 for Taiwanese companies, 0.34 for foreign companies, 0.34 for near-sea shipping, and 0.29 for deep-sea shipping.

Table 5. Numerical causal relationships of key influencing factors for the introduction of autonomous ships in container shipping.

Factor	${\mathit{D}}_{\mathit{k}}$				${\mathit{R}}_{\mathit{k}}$				${\mathit{D}}_{\mathit{k}}+{\mathit{R}}_{\mathit{k}}$				${\mathit{D}}_{\mathit{k}}-{\mathit{R}}_{\mathit{k}}$
	Taiwan	Foreign	Near	Deep	Taiwan	Foreign	Near	Deep	Taiwan	Foreign	Near	Deep	Taiwan	Foreign	Near	Deep
B1	0	0.3930	-	0	1.2076	1.1370	-	1.2138	1.2076	1.5300	-	1.2138	−1.2076	−0.7440	-	−1.2138
B2	-	0	-	0	-	0.7671	-	0.2812	-	0.7671	-	0.2812	-	−0.7671	-	−0.2812
B3	-	0.2127	0	0.2987	-	0.1828	1.1550	0	-	0.3955	1.1550	0.2987	-	0.0299	−1.1550	0.2987
B4	-	0.2205	0	-	-	0	0.3886	-	-	0.2205	0.3886	-	-	0.2205	−0.3886	-
O2	1.2424	-	1.1608	0.6016	0	-	0	0	1.2424	-	1.1608	0.6016	1.2424	-	1.1608	0.6016
O3	0.3926	0.2013	0.3897	0.3052	0.3953	0	0	0	0.7879	0.2013	0.3897	0.3052	−0.0027	0.2013	0.3897	0.3052
C2	0.3932	-	0.3815	0.2966	0.4253	-	0.3864	0.2883	0.8185	-	0.7679	0.5849	−0.0321	-	−0.0049	0.0083
R2	-	-	-	0.2812	-	-	-	0	-	-	-	0.2812	-	-		0.2812
R3	-	0.1910	-	-	-	0	-	-	-	0.1910	-	-	-	0.1910	-	-

and

Table 6. Numerical causal relationships of key influencing factors for the introduction of autonomous ships in bulk shipping.

Factor	${\mathit{D}}_{\mathit{k}}$				${\mathit{R}}_{\mathit{k}}$				${\mathit{D}}_{\mathit{k}}+{\mathit{R}}_{\mathit{k}}$				${\mathit{D}}_{\mathit{k}}-{\mathit{R}}_{\mathit{k}}$
	Taiwan	Foreign	Near	Deep	Taiwan	Foreign	Near	Deep	Taiwan	Foreign	Near	Deep	Taiwan	Foreign	Near	Deep
B1	0.3855	0	0.7121	0	0	1.4040	0.3453	0.2951	0.3855	1.4040	1.0574	0.2951	0.3855	−1.4040	0.3668	−0.2951
B2	0.7842	1.0770	1.0406	0.5922	0.7656	0.3461	1.0399	0	1.5498	1.4231	2.0805	0.5922	0.0186	0.7309	0.0007	0.5922
B3	-	-	0.3420	-	-	-	0	-	-	-	0.3420	-	-	-	0.3420	-
O2	1.1540	0.7016	0.6849	0.9517	0	0.3468	0.3468	0.2936	1.1540	1.0624	1.0317	1.2453	1.1540	0.3408	0.3381	0.6581
O3	-	-	0.3431	-	-	-	0	-	-	-	0.3431	-	-	-	0.3431	-
C2	0	0.3553	0	0.2936	1.1517	1.4265	1.3907	0.6282	1.1517	1.7818	1.3907	0.9218	−1.1517	−1.0712	−1.3907	−0.3346
R2	0	0.3429	-	0.2951	1.1684	0	-	0.6139	1.1684	0.3429	-	0.9090	−1.1684	0.3429	-	−0.3188
R4	0.7620	-	-	-	0	-	-	-	0.7620	-	-	-	0.7620	-	-	-

show the numerical causal relationships of key influencing factors for the introduction of autonomous ships by different types of shipping companies.

Table 5 and Table 6 show that, except for foreign container shipping companies, foreign bulk shipping companies, and bulk shipping companies for near-ocean routes, the primary influencing factor is “ship technology development”.

The primary influencing factor for foreign container shipping companies is “reliable scheduling”. For foreign bulk shipping companies, it is “enhancing personal and ship safety”. For near-sea bulk shipping companies, it is “enhancing operational performance”.

Regarding key affected factors, Taiwanese container shipping companies, deep-sea container shipping companies, and foreign bulk shipping companies are all affected by “enhancing operational performance”.

Near-sea bulk shipping companies are affected by “investment and development costs”, while foreign container shipping companies, near-sea container shipping companies, and Taiwanese bulk shipping companies are affected by “enhancing personal and ship safety”, “reducing operational costs”, and “emergency response capability”, respectively. To understand the causal relationships between the main influencing and affected factors for container and bulk shippers of different attributes, refer to Figure 5, Figure 6, Figure 7, Figure 8, Figure 9, Figure 10, Figure 11 and Figure 12.

“Ship technology development”, “investment and development costs”, and “port infrastructure” are factors that influence “enhancing operational performance” for Taiwanese container shipping companies. Figure 5 shows the causal relationships of key influencing factors for Taiwanese container shipping companies’ introduction of autonomous ships.

In foreign container shipping, “enhancing operational performance”, “cargo handling obligations”, and “emergency response capability” are factors that influence “enhancing personal and ship safety” for foreign container shipping companies. Figure 6 shows the causal relationships of key factors that influence foreign container shipping companies’ introduction of autonomous ships.

“Port infrastructure”, “ship technology development”, and “investment and development costs” are factors influencing the “reduction of operational costs” for near-sea container shipping companies. The causal relationships of key influencing factors for introducing autonomous ships by near-sea container shipping companies are shown in Figure 7.

“Ship technology development”, “port infrastructure”, “reduction of operational costs”, and “investment and development costs” are factors that influence “enhancing operational performance” for deep-sea container shipping companies. Figure 8 shows the causal relationships of key influencing factors for the introduction of autonomous ships by deep-sea container shipping companies.

“Enhancing personal and ship safety”, “ship technology development”, and “social acceptance” are factors influencing the “emergency response capability” of Taiwanese bulk shipping companies. The causal relationships of key influencing factors for the introduction of autonomous ships by Taiwanese bulk shipping companies are shown in Figure 9.

“Enhancing personal and ship safety”, “ship technology development”, “improving operational performance”, and “emergency response capability” are factors influencing foreign bulk shipping companies’ “investment and development costs”. Figure 10 shows the causal relationships of key influencing factors for introducing autonomous ships by foreign bulk shipping companies.

“Improving operational performance”, “enhancing personal and ship safety”, and “reducing operational costs” are factors influencing the “investment and development costs” of near-sea shipping companies. Figure 11 shows the causal relationships of key influencing factors for introducing autonomous ships by near-sea shipping companies.

“Ship technology development” and “enhancing personal and ship safety” are factors influencing the “investment and development costs” of deep-sea bulk shipping companies. The causal relationships of key influencing factors for introducing autonomous ships by deep-sea bulk shipping companies are shown in Figure 12.

4.4. Implications for the Maritime Industry

In summary, regarding the importance of key influencing factors in both the overall and subgroup analyses, “emergency response capability” and “regulatory deficiencies” are critical factors affecting shipping companies’ adoption of autonomous ships. The handling of maritime emergencies generally includes emergency stopping, course changes, speed reduction for avoidance, ship and engine inspection, automatic berthing, night navigation capability, collision prediction, proactive collision avoidance, and automated course adjustment. This highlights that establishing an effective emergency rescue system for autonomous ships is a crucial issue that needs urgent resolution in the field of autonomous shipping. Developing a comprehensive emergency transport rescue system can expedite responses to incidents, such as rescuing casualties, vessels, and cargo, for prompt subsequent resumption of transport operations. This also would help prevent secondary accidents and delays, effectively controlling casualties and reducing losses caused by accidents. This is vital for the future development of autonomous ships and shipping companies’ decision-making regarding their potential adoption.

In maritime operations, container ships frequently call at ports for loading and unloading, increasing the likelihood of inspections by regulatory authorities, such as Port State Control (PSC). As a result, container shipping operators, especially those on short-sea routes, focus on ensuring that regulations regarding autonomous vessels in different jurisdictions are complete and well documented for compliance. Conversely, bulk carriers often undertake longer voyages with fewer port calls, spending extended periods at sea. This necessitates robust capabilities in handling onboard malfunctions, emergency repairs, and cargo salvage during voyages. The prioritization of key factors also varies between Taiwanese and foreign container shipping operators, influenced by ship inspection standards, navigation regulations, and liability allocations for crew safety, vessel integrity, and cargo.

The ability of autonomous ships to identify and respond to maritime emergencies primarily relies on onboard sensors that capture and analyze current maritime conditions and images in real time. This enables effective detection, identification, simulation, decision-making, information dissemination, and the evaluation and storage of decisions. Repeated execution of these processes will enhance the handling of similar future emergencies regarding response capability and speed. The transmission of sensor data requires integration with the Internet and communication networks such as 5G and 6G, ensuring the seamless combination of high-performance and high-quality audio and visual data.

In addition to advanced management technologies, comprehensive management equipment, and professional management personnel, the emergency response system of autonomous ships must also improve the timeliness, effectiveness, and reliability of information to prevent exacerbating the consequences of an incident or causing unnecessary resource waste. “Effective emergency response” will not only facilitate the smooth adoption of autonomous ship operations by shipping companies but also alleviate public skepticism, reduce delays in shipping and cargo, and minimize environmental damage caused by accidents, thereby enhancing social benefits and acceptance.

The primary mission of an autonomous ship’s control center is to coordinate information, provide countermeasures, and manage rescue operations during incidents, ensuring that the response process proceeds in an orderly manner. Therefore, control center personnel must undergo regular training to ensure that functions related to procurement, alert systems, regulations, human and vessel safety, and environmental conservation continue to operate effectively.

During an emergency response, it is crucial to ensure the availability of necessary equipment and resources, conduct incident analysis, and manage the deployment and coordination of available resources. This relies on analyzing information needs and building and developing both software and hardware systems to enable the accurate identification of emergency locations, analysis of rescue complexities, determination of optimal routes, confirmation of necessary medical and firefighting resources, and effective transmission and storage of related information.

An effective national regulatory policy response has been limited compared to the development and trial operations of autonomous ship technology, with the allocation of collision liability being the most critical issue. Since the impact of autonomous ship technology on shipping cannot be fully anticipated during the early stages of technological development, there is also a concern about technology becoming an integral part of the industry by the time significant incidents occur.

Therefore, thorough and comprehensive regulations are required ahead of time to address issues that may arise from autonomous vessels, including ethical and moral implications, navigational safety, final decision-making and control authority, cybersecurity, and legal liabilities. Additionally, there must be provisions for adjustments in related funding, technology, and navigation port infrastructure to accommodate autonomous ships.

Therefore, this study recommends that Taiwan’s legislative model initially focus on abstract regulations, supplemented later with specific legislation detailing rights, responsibilities, and control measures. This approach aims to gradually establish a framework for autonomous ships, setting core objectives and principles first and then enacting specific laws as needed in various fields. Simultaneously, education and awareness about autonomous shipping should be promoted.

Additionally, a committee should be established under the Ministry of Transportation and Communication to provide recommendations and reports on relevant budget and regulatory matters, including commercial shipping activities, energy affairs, port infrastructure, and financial and insurance institutions. The main goals are to create economic incentives, establish information exchange channels, and foster interagency cooperation.

In terms of influencing factor relationships, besides the aforementioned emergency response capabilities, the primary influencing factor is the development of ship technology, followed sequentially by enhancing personal and ship safety, improving operational performance, investment and development costs, and port infrastructure. Autonomous ships represent a new type of transport service that unifies multiple objectives such as safe navigation, route optimization, assurance of personal and ship safety, transportation information exchange, and energy saving and carbon reduction. Through the efforts of various countries, this transportation mode has already begun to emerge and manifest itself in certain specialized areas. However, in the Asia–Pacific region, autonomous shipping is still in the early growth stages. Nevertheless, with the ongoing research and application in other places, the future market demand for autonomous ships in the Asia–Pacific is expected to begin to flourish gradually.

Regarding “ship technology development”, the primary technologies required for autonomous ships include sensors, computer vision, communication, control, and positioning and navigation technologies.

• Sensor Technology: utilizes laser, vision, sonar sensors, and radio detection and ranging to determine ship position, ship status, weather conditions (speed, wind force, fog), and obstacle detection.
• Computer Vision Technology: combines 3D measurement and image recognition technologies to effectively identify obstacles and measure their distance from ships, detect ship speed, and enhance emergency incident handling capabilities.
• Communication Technology: uses wired and wireless communications to accurately transmit image and voice information to the control center for subsequent evaluation and decision-making.
• Control Technology: integrates information collected by various sensors, enabling the ship’s computer system to have automatic recognition, expanded vision, longitudinal and transverse collision avoidance in maritime and port areas, ship safety detection, and automatic navigation capabilities.
• Positioning and Navigation Technology: utilizes geographic positioning systems, ship positioning systems, and port vessel scheduling systems to provide real-time and periodic dynamic information (such as ship position, ship speed, real-time maritime climate information, power equipment information) and static information (such as route information, port information, schedule information).

To enhance the safety of ships and crews, autonomous ships use onboard cameras to detect obstacles in advance, employing deep learning algorithms to recognize nearby vessels and track their positions and trajectories to determine their speed and direction. The system can then predict and prevent potential collision hazards should the ships happen to approach each other. Cameras installed in fishing port areas and on ships capture real-time traffic information, which is uploaded to a cloud database to aid shipping companies and port authorities in early monitoring and resolving any potential navigational safety issues. Only through the collaborative construction of a safe and intelligent maritime system by the shipping industry and ports can the future goals of zero accidents, zero congestion, and zero pollution in maritime transportation be achieved.

To enhance operational performance, Internet of Things (IoT) integration maps various real-world information and changes (such as climate, images, temperature, and sound) through digital software, allowing the control center to constantly monitor the actual conditions of ships in the real world. However, the current differences and incompatibilities among various national maritime systems are among the most significant obstacles to developing autonomous ships. In the short term, efforts should be directed to promote smart maritime integration, including systems for emergency management, ship navigation, fleet management, personnel and ship coordination, route information, port monitoring, collision avoidance, and cruising. By integrating and optimizing global maritime information systems with IoT technology, satellite navigation, sensor technology, propulsion technology, and alternative energy technology, it will be possible to achieve efficient, safe, environmentally friendly, and punctual maritime transport services.

Regarding investment and development costs, the core competitiveness of the shipping industry still lies in transportation itself. The primary value of autonomous ship technology is in “enhancing operational performance”, “improving personal and ship safety”, “conserving the environment”, and “stabilizing schedules”, which, in turn, will “reduce labor and operational costs”. However, autonomous ship technology research and development, ship prices, related port infrastructure, and information systems all demand significant capital investment. In addition to government support, it also necessitates backing from large shipping companies and cooperation with research institutions to establish a long-term, stable investment strategy and plan a timeline for introducing autonomous ships.

With the advancement of autonomous ship development technology, shipping companies have higher demands and requirements for introducing numerous information systems. These include autonomous ship communication application technology; cooperation technology for routes, vessels, and inter-vessel operations; mobile Internet technology; and information security management. These systems can enhance the benefits and opportunities brought by autonomous ships, such as “enhanced personal and ship safety”, “ship technology development”, and “environmental conservation”.

They also help reduce the costs and risks associated with autonomous ships, such as “emergency response capability”, “incomplete regulations”, and “cargo care obligations”. This, in turn, effectively improves transportation efficiency, stabilizes shipping schedules, and significantly enhances personal and ship safety. Furthermore, this development necessitates the accelerated cross-industry integration of the shipping industry, including shipbuilding, maintenance, shipping companies, and port management in both physical and online information services.

5. Conclusions and Recommendations

5.1. Conclusions

• The rapid changes in shipping technology are likely to lead to many different problems. Automation is gradually replacing many jobs in shipping-related industries, and ships are moving from Level 1 (LV1) to Level 2 (LV2) automation, eventually transitioning to Level 4 (LV4). This transition signifies the need for innovation and changes in the number and skills of seafarers required on ships, the need for making port communication and shipbuilding technology more intelligent, as well as increasing the willingness of and investment from shipping companies toward developing and adopting these technologies, to enable a gradual entry into the next stage of shipping automation. This study has found only minimal differences between shipping companies overall and container and bulk shippers in terms of the key influencing factors’ overall importance and relevance. Only in the subgroup analysis of the relevance of the influencing factors can minor differences be observed among the different groups. This indicates a consensus among shipping companies at the current stage regarding developing and adopting autonomous ships.
• Regarding the importance of key influencing factors, the overall ranking places “emergency response capability” as the most critical, followed by “incomplete regulations” and “cargo care obligations”. For container shipping companies, the overall importance ranking places “incomplete regulations” as the top priority, followed by “emergency response capability” and “improving personal and ship safety”. The ranking for bulk shipping aligns with the importance ranking for shipping companies overall.
• In the subgroup analysis of key influencing factors, the primary key factor affecting the introduction of autonomous ships for near-sea container shipping companies and foreign container shipping companies is “incomplete regulations”. “Emergency response capability” is the critical factor influencing the adoption of autonomous ships by deep-sea container shipping companies, Taiwanese container shipping companies, near-sea bulk shipping, deep-sea bulk shipping, Taiwanese bulk shipping, and foreign bulk shipping.
• In the correlation analysis of overall, container, and bulk shipping, the “development of ship technology” is the primary influencing factor for all sectors. “Improving operational performance” is the main affected factor for overall and container shipping companies, while “enhancing personal and ship safety” is the main affected factor for bulk shipping.
• In the subgroup correlation analysis, the “development of ship technology” and “reducing operational costs” are the primary influencing and affected factors for near-sea container shipping companies. “Development of ship technology” and “improving operational performance” are deep-sea container shipping companies’ primary influencing and affected factors. “Development of ship technology” and “investment and development costs” are Taiwanese container shipping companies’ primary influencing and affected factors. “Schedule reliability” and “enhancing personal and ship safety” are foreign container shipping companies’ primary influencing and affected factors. “Improving operational performance” and “investment and development costs” are the primary influencing and affected factors for near-sea bulk shipping companies. “Development of ship technology” and “investment and development costs” are deep-sea bulk shipping companies’ primary influencing and affected factors. “Development of ship technology” and “emergency response capability” are the primary influencing and affected factors for Taiwanese bulk shipping companies. “Enhancing personal and ship safety” and “improving operational performance” are foreign bulk shipping companies’ primary influencing and affected factors.

5.2. Recommendations

Climate change has triggered a transformation in economic, societal, and legal frameworks, with autonomous shipping emerging to achieve a low-carbon sustainable marine environment. In recent years, Taiwan’s environmental conservation policies and legal frameworks have increasingly focused on issues such as greenhouse gas emissions (Basic Environment Act) and renewable energy (Renewable Energy Development Act) but have not fully met the legislative requirements for autonomous shipping. The complexities, depth, and breadth involved in developing autonomous shipping technology, investment policy formulation, ecological environment, and crew–vessel safety are largely unknown, leading to a tendency among Taiwanese and most international shipping companies to adopt a wait-and-see approach. This has resulted in slow or inadequate policy and regulatory responses, particularly given the unique but currently non-urgent and non-essential stage of autonomous shipping. The subsequent development and application scope of autonomous shipping will influence the evolution of international and Taiwanese legislation in various countries, warranting ongoing attention in subsequent research endeavors.

Furthermore, the application of autonomous shipping involves multiple issues, with technology development and continuous advancement being influenced by high costs and national transportation policies, as well as the allocation of national budgets. Stakeholders, including autonomous ship developers, manufacturers, shipping companies, and relevant governmental bodies, should focus on vessel research and development, technology integration, coordination and cooperation, financing, legislation, and education to transform the current negative impacts of autonomous shipping into opportunities for future development. Integrating augmented reality (AR), virtual reality (VR), and 3D and extensive Internet-based technologies into a metaverse could become a key driver supporting the development of autonomous shipping technology. However, the current development of this metaverse is limited by significant costs and energy consumption. Issues such as carbon neutrality, green energy, green shipping, and green finance are also worth further research and attention.

To address challenges in obtaining expert survey responses, particularly when the number of responses is limited and extreme precision is unnecessary, this study recommends calculating the Consistency Ratio of the Hierarchy (C.R.H.) using the Consistency Index (C.I.H.) and Random Index (R.I.H.) to achieve overall consistency across the hierarchical structure, preventing the dismissal of individual questionnaires due to partial inconsistency in their results. This approach minimizes potential biases that might otherwise impact the research outcomes.

This research primarily focused on major bulk and container shipping companies with deadweight tonnage and capacity while acknowledging variations in regulatory principles across different jurisdictions. Expanding future studies to include perspectives from operators in the Americas, Oceania, and Africa, as well as analyzing how domestic regulations operate within a unified international framework, could significantly broaden and refine the research scope and results.