Next Article in Journal
Do Rock Design Formulas Based on Wave Flume Experiments Reliably Model Their Performance at Sea?
Next Article in Special Issue
Energetic and Ecological Effects of the Slow Steaming Application and Gasification of Container Ships
Previous Article in Journal
Underwater Absorber for a Remotely Operated Vehicle
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Digitalization in Maritime Transport and Seaports: Bibliometric, Content and Thematic Analysis

1
Institute of Shipping Economics and Logistics, 28359 Bremen, Germany
2
Faculty of Maritime Studies, University of Rijeka, 51000 Rijeka, Croatia
3
Faculty of Organizational Sciences, University of Maribor, 4000 Kranj, Slovenia
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2022, 10(4), 486; https://doi.org/10.3390/jmse10040486
Submission received: 26 February 2022 / Revised: 22 March 2022 / Accepted: 23 March 2022 / Published: 1 April 2022
(This article belongs to the Special Issue Marine Navigation and Safety at Sea)

Abstract

:
In this paper, a bibliometric, content and thematic analysis of digitalization in maritime transport and seaports was performed. The research was primarily motivated by the scarcity of similar works offering a comprehensive and recent literature analysis, the advancements of the maritime digitalization itself, and its influence on all related processes. The initial investigation phase considered 8178 publications, leading through the research steps to the final inclusion of 280 papers, the thematic and content analysis of which were performed using various bibliometric tools. The research encompassed various criteria, ranging through databases, keywords, topics, research areas and others. The resulting concept map emphasized the main concepts that digitalization in maritime transport relies on, or strives towards. The aim of the study was to address the fundamental research questions, with the tendency to define the main key points in the current maritime transport and seaport digitalization process. It can be concluded that an increasing number of authors recognize the importance of new digital technologies in maritime transport and seaports. However, with new digital technologies come specific risks such as spoofing or data manipulation that need to be further analyzed.

1. Introduction

The maritime transport sector represents a backbone of the globalized economy [1], and its digitalization is moving at different dynamics in different domains [2]. Digitalization in the maritime transport sector refers to the implementation of a variety of digital technologies [3], which may provide the enhanced productivity, efficiency, sustainability of business processes [4], as well as transparency [5]. It may also provide a competitive advantage by connecting all of the involved stakeholders in the value chain [6]. Ships, seaports, and offshore facilities have become increasingly dependent on information and communication technologies [7]. Despite opportunities, the digitalization and digital transformation in the maritime transport sector and seaports is slower compared to other transport sectors [8].
After the analysis of the previous research on the topic of digitalization in maritime transport, it is possible to notice several directions. An analysis of digitalization in maritime transport was conducted by Sanchez-Gonzalez et al. (2019) [2], in which the authors elaborated upon shipbuilding and ship design, in addition to maritime transport. Most authors have analyzed the impacts of a single technology/solution in maritime transport and seaports, such as the impact of Blockchain [9,10,11], a Port Community System [12], or the Internet of Things [13]. For example, Aloini et al. [14] analyzed the role of process coordination dynamics and information exchanges in maritime logistics, for which a case study in a mid-sized port supported by a Port Community System was developed. One of the issues regarding maritime transport digitalization is the vulnerability to cyber attacks, which can lead to the loss of vessels’ control or the loss of sensitive data [15]. In this respect, digitalization brings along challenges that need to be addressed [16]. In certain instances [7,15,17,18,19], the security challenges of individual technologies or groups of technologies have been analyzed, such as phishing, malware, and data theft. Although these publications represent an important contribution to the current body of knowledge, there is a lack of a comprehensive overview of digitalization in maritime transport, with emphasis on the implementation of different ICT (Information and Communications Technology) solutions in various fields such as port operation planning, berth allocation, human resource planning, decision making, routing optimization, and information exchange.
In order to shape (plan) future research, it is important to first understand the current body of knowledge. The aims of this study were to provide a thorough overview of the current body of knowledge related to digitalization in maritime transport and seaports, considering the aforementioned fields, in order to address and provide answers to the main research questions arising from the digitalization process:
  • What are the key research areas dealing with digitalization in maritime transport and seaports?
  • What types of papers are most represented?
  • What type of research methodology is most used?
  • How many papers are published per year?
  • Which countries have actively participated in the research?
  • Which papers and authors are most cited?
  • Which research categories are most analyzed?
  • What are the keywords dealing with digitalization in maritime transport and seaports?
For this purpose, the bibliometric, content and thematic analysis of digitalization in maritime transport and seaports was conducted. In this way, it was possible to discover the research progress, the key themes of digitalization, and research gaps in maritime transport and seaports. Thus, this paper can assist scholars and practitioners in obtaining a comprehensive understanding of the status quo and further tendencies of digitalization in maritime transport and seaports.
The paper is structured as follows. Section 2 deals with related work and the most relevant literature referring to previous research in the field, in order to place the proposed study in the contribution context. Section 3 represents a systematic explanation of research analyses, and steps of particular phases. In Section 4 and Section 5, the final results are discussed through bibliometric, content ant thematic analyses, based on which the main findings are presented. The latter refer to answers to the addressed questions, expected future outcomes, and perspectives on digitalization in maritime transport.

2. Background

This section provides an overview of the existing papers in which literature review or bibliometric analysis were used as research methods. This step is important in order to further delineate the scientific gap(s) which is addressed in this study.
Through a systematic literature review, Sanchez-Gonzalez et al. (2019) [2] have claimed that maritime transport has been accepting digitalization according to different dynamics in the different fields. They have analyzed state-of-the-art of maritime transport industry digitalization, giving an overview for ship design/shipbuilding, shipping and seaports, and defining eight domains to which the digitalization is currently applicable: “autonomous vehicles and robotics; artificial intelligence (AI); Big Data; virtual reality, augmented and mixed reality; Internet of Things; the cloud and edge computing; digital security; 3D printing and additive engineering”. Their research has demonstrated that domains exist in which almost no formal research has been conducted so far, concluding that several major areas require attention, e.g., the integration of the studies on AI in the industry, and the use of robotics in maritime transport [2]. Although their focus has been on digitalization in maritime transport, they have also included, in their research, papers dealing with shipbuilding and ship design, which will not be analyzed in our paper. This paper focuses exclusively on the digitalization and implementation of various digital technologies in maritime transport and seaports. Given that digital technologies are rapidly evolving, the authors expect to identify new research directions in the field of digitalization.
Fruth and Teuteberg (2017) [20] provided a systematic literature review of the current state of digitalization in maritime logistics, and discussed existing problematic areas (e.g., the lack of theoretical studies regarding the future behavior of stakeholders in the maritime logistics chain). Furthermore, they presented potentials for improvement, e.g., by expanding their research into several areas where Big Data technology has already been implemented. Their research scope covered not only maritime transport, but also the logistics sector.
Bălan (2020) [21] conducted a literature review and focused on future advanced ICT in cargo maritime transport: Big Data, the Internet of Things, cloud computing, and autonomous vessels (including unmanned ships/vessels) [21]. The author claimed that advanced ICT will have a disruptive impact on maritime transport and supply chains in the future.
Gil et al. (2020) [22] used bibliometric methods to depict the domain of onboard Decision Support Systems (DSS) for operations focused on safety insurance and accident prevention. Despite valuable results, their research focused solely on DSS. The authors noted that maritime transport faces new challenges related to safety due to increasing traffic and ship size, respectively. They concluded that new concepts related to DSS which support safe shipping operations in the presence of reduced ship manning are rapidly growing, both in academia and in industry.
Tijan et al. (2021a) [23] performed a literature review of the drivers, success factors and barriers to digital transformation in the maritime transport sector. Due to a lack of research and scientific papers dealing with digital transformation in the maritime transport sector, the authors focused on publications which were not only related to digital transformation in the maritime transport sector but also to transport and digital transformation in general.
Yang et al. (2019) [24] conducted a literature review regarding features of an Automatic Identification System (AIS), dividing it into three development stages: basic, extended, and advanced applications. They suggest potential digitalization fostering using the system when it is combined with supplementary databases.
The above presented studies offer an important, but not comprehensive, overview of current research achievements and future research directions related to digitalization in the field of maritime transport and seaports.

3. Research Design

Bibliometric, content and thematic analysis methods can be applied to various research areas [25,26,27]. Bibliometric analysis can be defined as a quantitative analysis of books, articles, or other publications [26] to provide an overview of the current state on the particular topic. Content and thematic analysis helps to identify and visualize key research themes [27]. In order to provide a comprehensive overview of the current body of knowledge related to the digitalization in maritime transport and seaports, the authors established the methodological approach presented in Figure 1.
In the research design setup, the definition of the scope of the study was set together with aims, objectives and research questions (Section 1). The data collection and processing consisted of several consequent steps, including preliminary investigations, filtering based on inclusion and exclusion criteria, and reverse search analysis. The results were then obtained through the final inclusion of papers. Bibliometric, content and thematic analysis were conducted. In this respect, the authors analyzed the authors and their affiliations (by countries) actively participating in research, the most cited papers and authors, research categories and keywords, as well as key research themes. In the end, the authors provide conclusions and further considerations.

3.1. Selection of Papers

The research was conducted using Web of Science (WOS) and Scopus databases. The authors focused on 75 search strings (Appendix A), which were identified through preliminary research and a literature review. Furthermore, backward analysis was used to extend the set of relevant primary studies [28].
The inclusion and exclusion criteria that are shown in the Table 1 had to be determined in order to identify the most relevant articles.
As shown in Table 1, the authors focused on Topic or Title in WOS, and on the Article title, Abstract, and Keywords in Scopus. Furthermore, the following related fields were considered: ICT and other solutions/systems for port operation planning, berth allocation, human resources planning, decision making, routing optimization, and information exchange in the maritime transport sector and seaports. Publications referring to e-navigation and cybersecurity were considered as well. For general keywords such as “port”, which can be used both in the computing and transport, the authors limited the search to the following categories: “Transportation”, “Transport Science Technology”, “Decision Sciences”, and “Business, Management and Accounting”. Furthermore, in line with [25], the search was not limited to high-ranking periodical publications. Beside journals, the authors also considered conference papers and book chapters.
The exclusion criteria were applied to papers in which digitalization is both mentioned to a small extent and is not a main focus, such as [29,30]. Furthermore, papers on shipbuilding industry and ship design, and papers that explicitly referred to surveillance were not considered further. Non-English-language sources were excluded as well, in order to avoid the tentative regional overrepresentation of research [25]. After the described filtration, a total number of 280 papers was analyzed further.

3.2. Analysis

Two tools were used for the analyses of suitable papers. Recognising its features and abilities to facilitate the understanding of network and historical patterns, CiteSpace software was used to generate visual knowledge maps, including countries, research categories and keywords [26]. Among the features used, the authors considered the identification of the rapid-growth topical areas, the identification of geospatial collaboration patterns, and international collaboration [31].
Automated content (text) analysis was conducted using Leximancer software, which provided “concept maps” for the visualization of the results of the analyzed text. With embedded Bayesian learning algorithm’ relations between concepts were visualized and aggregated with related meanings into themes [27]. According to the methodology of Vidmar et al. (2021), all of the PDF files were first converted into text files, and then text which was not related to the content (e.g., authors and their affiliation, journal names, etc.) were deleted. Standard English “stopwords” (a list of common words excluded from analysis) were added. The function “merge word variants” was also used, which combines concepts that have the same stems into one concept (e.g., singular and plural words such as port and ports are treated as one concept).

4. Results

4.1. Number of Papers per Year, and Countries

An analysis on an annual basis (Figure 2) showed that the maximum number of selected papers was published in 2020 (60 papers), followed by 2019 (40 papers), 2017 (27 papers), and 2016 (23 papers). In 2021, only 26 papers were published, as the analysis covered papers published before October 2021.
The “country cooperation network” is presented in Figure 3. Within the figure, the size of the letters indicates the representation of authors with affiliations from a particular country, while the larger purple circles indicating higher centrality.
As shown in Table 2, the authors with Croatian affiliation published the largest number of selected academic papers (40 papers in total), followed by the authors with German and English affiliations. High centrality implied the importance of the nodes. The centrality of authors with German affiliation reached 0.77, indicating that they kept a wide range of cooperation with authors affiliated with various countries such as Italy, Sweden, Colombia, and the People’s Republic of China, etc. On the other hand, although authors with Croatian affiliation produced the largest output of academic papers, their poor collaboration with authors affiliated with other countries is visible, with the centrality of 0.07.
After this step, further analysis was performed.

4.2. Analysis by Paper Type

The largest number of publications were journal papers (176), followed by conference papers (94) and book chapters (10), as presented in Table 3.
Most of the conference papers were presented at conferences held in Poland (11 papers), followed by Croatia (8 papers), and Russia (6 papers), as shown in Table 4.
Considering journal papers and countries, most of the papers were published in journals from England (62 in total), followed by Croatia (21) and and Netherlands (20), as shown in Table 5.
In order to recognize the core field journals, the number of articles per journal was calculated (Table 6). TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation (Poland), with 14 published papers (accounting for 7.95%), is closely followed by the Scientific Journal of Maritime Research (Croatia) with 13 papers (accounting for 7.39%).
The classification (Figure 4) shows that 73 publications were theoretical, 76 were qualitative, and 43 were quantitative, while 88 publications included mixed methods. The classification was made according to [27].
The theoretical publications mainly included frameworks based on literature and practice reviews, and the analysis of key research fields. Qualitative research mostly referred to case studies and empirically-based simulations. Regarding quantitative publications, they mainly included surveys or manipulated pre-existing statistical data using various statistical methods.

4.3. Analysis of the Most Cited Papers and Authors

As per the WOS and Scopus databases, the authors had to analyze separately the most cited papers and contributors due to several reasons. Certain papers which were included in Scopus were not indexed in WOS, and vice versa. In addition, in all cases, for the same paper, the citation numbers between the two databases differed.
In WOS, the most cited paper is “A fuzzy logic method for collision avoidance in Vessel Traffic Service” [32], with 95 citations, followed by “Maritime shipping digitalization: Blockchain-based technology applications, future improvements, and intention to use” [33], with 66 citations (Table 7).
In Scopus, the most cited paper was the same as it was previously, with 101 citations, followed by the paper “The importance of information technology in port terminal operations” [34], with 80 citations (Table 8).

4.4. Analysis of the Categories

Categories can reflect the development level of research on a specific subject during a given period [26]. The related literature in both databases was comprised of approximately 75 subject categories, the most frequent of which are shown in Figure 5.
The top five subject categories (Table 9) include Transportation, Engineering, Computer Science, Business and Economics, and Transportation Science and Technology. The distribution of the categories suggests that issues in transportation, engineering, computer science, business and economics were highly prioritized in research.

4.5. Analysis of the Keywords

In the analysis, the authors included Author Keywords and Keywords Plus in the Term Source field. Once the synonyms for each term were merged (e.g., “Port Community System” and “PCS”), the keywords emerged as shown in Table 10.
The most prominent keyword in the field of digitalization in maritime transport and seaports was the term “Port”, with the highest frequency (41). It was followed by the terms “Port Community System” (27), and “Big Data” (26). In terms of technologies, the keyword “Port Community System” appeared the most times, and it was the term used in one of the 75 search strings the authors used for this topic. However, “Big Data” was not one of the chosen keywords. Nevertheless, it was at the very top according to frequency. In this respect, modern technologies such as the Internet of Things, Big Data, and Blockchain, etc., are also playing an increasing role in the digital transformation in the maritime transport sector and seaports.

4.6. Content and Thematic Analysis

During the content and thematic analysis, 15 themes were identified (as shown in Figure 6); the order descends according to the number of matches from the analyzed text.
The themes are the following: “system”, “technology”, “data”, “port”, “ship”, “study”, “stakeholders”, “time”, “container”, “model”, “network”, “organizations”, “innovation”, “goods”, and “AIS”.
The “concept map” is shown in Figure 7.
The concept map consists of themes (colored circles) and the concepts that form each theme (the black text within the themes). The importance of the themes is shown as a “heat map” (the brighter the theme, the more often it was found in the analyzed text) and size (the larger the theme, the more concepts were combined in it) [27].
The concept map also shows the overlapping of the themes, e.g., “technology” and “innovation”, and which concepts are shared between two themes. Equally, the concept “digital” lies in the overlap of the themes “technology” and “innovation”, along with which relationships between the concepts maintain relationships between the themes, e.g., “process”, “digital”, “adoption” and “innovation”.
As the themes “technology” and “system” have the highest number of occurrences, and for the sake of clarity, the results were discussed first from a technology and then from a system perspective.

4.6.1. The “Technology” Perspective

The concept map shows that the theme “technology” overlaps with the following themes:
  • “Study”: Three concepts are shared between the themes “technology” and “study”, namely, “open”, “industry”, and “solutions”. Two paths connect the themes “technology” and “study”. The first path which connects the themes “technology” and “study” leads through the concepts “maritime”, “technology”, “industry”, “study” and “research” or “case”, and confirms that the research regarding the influence of technologies on maritime industry already exists. In addition, a larger number of studies are case studies, such as [35,36,37,38,39,40].
    A similar explanation is related to the second path, which links the themes “technology” and “study” through the concepts of “technology” and “solutions”.
  • “Innovation”: The concepts “digital” and “adoption” were shared between the themes “technology” and “innovation”, with two paths connecting “technology” and “innovation”. The first path leads through the concepts of “process”, “digital”, “adoption”, “innovation”, and “value”. Several authors connect digital technology with added value. For example, [41] claims that digital technology implementation results in a higher overall customer perceived value. According to [42], technology innovation works as a tool to integrate sustainability into the business model, creating long-term value.
    The second path leads through the concepts of “process”, “digital”, “adoption”, and “blockchain”. According to [43], intraorganizational factors such as company size, top management support and organizational structure affect the adoption of technological innovations. According to [41], trust is important for the adoption of new technology and open innovation. Furthermore, digitalization has been investigated increasingly, as the maritime industry is at the transition phase to a smarter and more digital environment surrounded by different digital innovations, including Blockchain adoption [41]. This statement is proven by the fact that several authors have recently researched the impact of Blockchain technology in the maritime transport sector, such as: [10,11,33,44,45,46,47,48,49,50,51].
  • “Organizations”: The concept of “business” is shared between the themes “technology” and “organizations”. Two paths connect the themes “technology” and “organizations”. The first path leads through the concepts “technology”, “business” and “organizations”. The second path leads through the concepts “technology”, “business” and “ICT”. The authors of [52] claim that ICT enables organizations to achieve a competitive advantage. According to [4], organizations are supported by innovative methods, technologies and tools that offer increasing flexibility and performance at lower prices. The authors of [23] claim that the integration of ICT systems may improve data exchange, management and business planning.
  • “Stakeholders” and “Port”: The term “chain” as a concept is shared between the “technology” and “stakeholders” themes. However, one path connects three themes: “technology”, “stakeholders” and “port”. It leads through the concepts of “implementation”, “chain”, logistics”, “companies”, and “services”, and continues its path toward “port” in one direction, and “seaport” stakeholders” and “community” in another. According to [42], the use of digitalization and new technologies improve the processes management, positively affecting the shipping companies’ long-term profits and generating a reputational advantage for stakeholders. According to [4], electronic data interchange has a strong impact on the overall logistics chain’s efficiency, as it speeds up business processes. However, huge investments in an appropriate IT “network” are required at the port level. The authors of [53] claim that supply chains are becoming more integrated, making hinterland operations more pronounced. In this respect, port authorities can facilitate the technology’s application, enabling better insights into traffic flows. Consequently, companies will be able to optimize their supply chains. The authors of [54] claim that the sea–land supply chain represents a promising field for researching the effects of digital technologies on operation management.
  • “Network” and “Data”: Two concepts are shared between themes “technology” and “network”, namely “maritime” and “smart”. However, two paths connect three themes: “technology”, “network” and “data”, which leads to the concepts: “technology”, “maritime”, “used”, and “data” in one direction, and “management” and “smart” in another. The authors of [55] claim that the wide implementation of novel technologies (e.g., Cyber–Physical Systems, the Internet of Things, and the Internet of Services) was included in Shipping 4.0, offering smarter embedded computers for onboard equipment, and providing a variety of new information and data, along with a range of shore facilities.

4.6.2. The “System” Perspective

The resulting overlapping of the “system” theme with others, as presented in Figure 7, is elaborated in the continuation.
  • “Port”: “Infrastructure” as a concept is shared between both the themes of “system” and “port”. In this respect, several studies analysed the following: [53] quoted the authors Hlali and Hammami, according to whom the seaport may be defined as a multidimensional system combining between an economical function, an infrastructure system, a geographical space and trade. John et al. (2018) [56] claim that seaport facilities may be considered as critical infrastructure systems which are vulnerable to various risks due to their complex structures. Thus, it is necessary to protect them from threats by using robust and sophisticated security systems, and measures for early detection. Regarding the themes “port” and “system”, several authors refer to the Port Community System, which improves data exchange between stakeholders, for example [12,19,36,57,58].
  • “Container”: The theme “container” overlaps with “port” and “system”. The authors of [59] analysed processes including the Port Community System, containers, and involved stakeholders. The authors of [60] mentioned the truck appointment system that enables truck drivers who want to deliver or collect containers at the terminal to provide, in advance, their administrative details to the terminal operator’s e-portal.
  • “Time”: One path connects themes “time” and “system”, which leads to the concepts “system”, “control” and “planning”, or “system”, “control”, and “vehicles”. For example, [61] claim that automated transfer vehicles are one of the most obvious examples of the importance of information and communication technologies in container terminals, as they allow a higher flow of containers and significantly reduce the time needed for serving ships.
Although the theme “system” does not overlap with the themes “ship” and “AIS”, the aforementioned themes are closely connected. For example, [62] analysed the system that controls and operates the ship, and which—among others—enables the monitoring of autonomous ships from onshore control centres. Furthermore, [63] analyzed 5G-based ship AIS intelligent control systems. The system can, among other features, process the information of vessels in the navigational area in a systematic manner, and can automatically arrange the vessel information in real time, providing feedback.

5. Discussion and Future Research Perspectives

In order to understand the frequency of publication, an analysis of published papers per year was conducted. The maximum number of selected papers was published in 2020. The development of new technologies is accelerating, and an increasing number of researchers are focusing on digitalization and the impact of digital technologies in maritime transport and seaports.
The authors with Croatian affiliation published the largest number of selected academic papers, followed by authors with German and English affiliations. The authors with German affiliation maintained extensive cooperation with authors affiliated with other countries, including Italy, Sweden, Colombia, the People’s Republic of China, and others. On the other hand, despite the fact that the authors with Croatian affiliation published the most papers, there was a lack of engagement with authors affiliated with other countries.
Regarding the conference papers and countries, most conference papers were presented at conferences held in Poland, followed by Croatia and Russia. In order to recognize core field journals, the number of articles per journal was calculated. According to the results, journals focused on maritime transport had the most published papers. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation (Poland), was the journal with the largest number of published papers relating to this research.
Comparing the Web of Science and the Scopus citations, a different number of citations for the same publications is visible. There are several reasons for this, such as the citations being affected by the size of the databases, and different citation practices between publication types existing, etc. Despite this, in both databases, the most cited paper was “A fuzzy logic method for collision avoidance in Vessel Traffic Service” [32]. The top five subject categories are Transportation, Engineering, Computer Science, Business and Economics, and Transportation Science and Technology.
It is necessary to compare the results from the Leximancer tool, which was used to analyse key concepts and themes, and the CiteSpace tool, which was used to analyse categories. Several differences can be noted. Firstly, full access to a paper is a precondition for the detailed analysis. The results from Leximancer are not terms consisting of several words. In this respect, it is necessary to compose a term from the obtained results (words i.e., concepts or themes). For example, in CiteSpace, one of the top 10 keywords is “Information System”. In Leximancer, “system” is a theme, and “information” is a concept which is part of the aforementioned theme. The words that are most often repeated in both tools are: Port, Community, System, Data, Information, Chain, Model, Management, Technology, Blockchain, Ship, Logistics, and Smart. However, in Leximancer, “Internet of Things”, “Big Data” and “Artificial Intelligence” are missing. For comparison, in CiteSpace, the keyword “Big Data” is in third place in terms of frequency. On the other hand, two of the concepts are “ship” and “autonomous” in the Leximancer tool. In CiteSpace, the keyword “autonomous ships” is not among the 17 most frequently mentioned keywords. In this respect, for a successful analysis, it was necessary to approach the topic from several perspectives.
Through the analysis of keywords, categories and themes, the most and least researched areas were identified. Based on this, topics that are not explored well enough (as they have the lowest number of hits) and should be more researched at the level of the maritime transport sector will be explained below.

5.1. Future Research Directions Based on the Results Obtained by CiteSpace and Leximancer

As was already mentioned, the Leximancer results differ from the CiteSpace results. In other words, using Leximancer, the most frequently mentioned terms are isolated, i.e., they formed the so-called groups; however, in order to understand the meaning of these terms, it was necessary to manually review the papers and derive a conclusion (which may be considered as one of the limitations). For example, the term “goods” is not the main focus” in numerous papers, but this term is mentioned when introducing a particular issue. Therefore, it was necessary to analyse the publications that contain the term “goods” in order to be able to derive future research directions.
On the other hand, regarding the CiteSpace results, the term can contain more words. In this case, for future research directions, the authors also used the terms with the lowest number of hits based on keywords; however, some keywords were not included, such as: “efficient”, “usage”, and “marine transport”, etc., because it was not possible to derive meaningful conclusions (which may represent a limitation of the CiteSpace tool). Our suggested future research directions were also based on future research directions suggested by authors in their papers.
Table 11 shows the terms that had the lowest number of hits in Leximancer and CiteSpace. In addition, Table 11 shows what has been researched in the papers and what is missing in the papers, on the basis of which future research directions have been presented.
It can be noticed that—in some publications—the terms are overlapping, as in the case of “innovation” and “organization”. Organizations are most often mentioned in the context of “cooperation between organizations”, “specifics of individual organizations”, and “competitiveness between organizations”, etc., but as introductory sentences to a particular issue.

5.2. Future Research Directions Related to Advanced Digital Technologies in Maritime Transport

Despite the numerous benefits that Leximancer and CiteSpace provide, it was necessary to read the publications manually in order to ensure that all of the important publications were included. What was missing were, for example, the keywords that mention “5G”, which is a promising research direction. In this regard, the authors have singled out some topics that could be analysed. These topics refer to advanced digital technologies which represent the main drivers of digitalization and digital transformation [23]. The importance of advanced technologies has been recognized by the European Commission as well. In this respect, certain strategies have been developed, such as the AI strategy, which aims to streamline research and policy options for AI regulation [85].
As was demonstrated by the keyword analysis, Artificial intelligence is gaining increasing attention from a number of researchers. AI is usually mentioned in combination with other digital technologies. The combination of AI and “unnamed vessels” refers to vessels that can learn from situations, and can consequently plan and implement a journey. However, AI implemented in procedures related to unmanned vessels can be dangerous. It is important to explore what cybersecurity measures need to be implemented in order to avoid negative consequences separately for various system types (e.g., storyless systems). In addition, one of the research directions is the definition of digital technologies’ combinations which is required in order to minimize or eliminate the negative consequences, such as data breaching, spoofing, or data manipulation. Furthermore, with the combination of AI and Big Data, it is possible to improve the usage of all of the available information. However, possible policy guidelines need to be explored further in order to reap the full benefits of such technologies without compromising data security and privacy.
The lack of research was noticed in the field of AI and optimized port operations. One of the AI applications in seaport operations was analysed by [3]. According to their research, AI can be used determine which container to stack or unload first.
Regarding Artificial Intelligence and machine learning, this combination led to the creation of smart AI-enabled automation systems that can process large amounts of data, evaluate alternatives, and execute decisions [76]. However, machine learning is sensitive to errors, which can go undetected for a long time. Therefore, future research should focus on safety related to machine learning in combination with artificial intelligence. Artificial intelligence and sensors can also be combined. This combination may enable improved decision making, optimized business processes, and reduced harmful environmental impacts.
According to [86], AIS data combined with various artificial intelligence techniques will play an important role in shipping analysis services. It will be easier to approach strategic and operational information on any vessel or fleet of vessels at the global level. However, despite the numerous system opportunities, AIS has numerous vulnerabilities and pitfalls, as it is an open system transmitting on dedicated VHF frequencies. Further research is needed in order to ensure that data can be used without negative consequences (such as AIS spoofing).
The applications and benefits of the Internet of Things in the maritime transport sector are widely analysed, and their shortcomings should also be considered. For instance, the combination of the Internet of Things and sensors may provide data on cargo status in a timely manner, which consequently improves decision making. On the other hand, there appears to be an increased risk of security breaches and potential data manipulation.
Another research direction may be focused on smart ports and automation. The combination of various digital technologies and automation may improve monitoring, control, and planning of business processes in the maritime transport sector and seaports. However, it is necessary to bear in mind that “the more complex the system, the greater the probability of errors and disturbances in the system” [87].
A promising research direction is 5G network application in maritime transport. The authors of [63] analyzed a 5G communication ship traffic intelligent analysis platform, which can fundamentally strengthen the performance related to information collection. If 5G and AI are combined, the AI vulnerabilities may be exploited in cyber-attacks, while the “deployment of 5G network infrastructure will expand the attack surface area” [7].
Although PCSs have already been implemented in numerous ports, with the advent of new digital technologies it is possible to expand the PCSs’ functionalities. For example, Blockchain technology may foster business processes and cost reductions. On the other hand, and especially if there are weak network security measures, PCSs are prone to intentional attacks. In this context, it is important to analyse what combination of digital technologies and security measures are required in order to minimize various types of negative consequences.
Several limitations exist in the paper. First of all, the tools recognized only the most frequent keywords, relations between them, and conceptualizations of themes. The research is based on a literature review and, considering the nature and evolution trends of the elaborated topic, the presented state-of-the art could soon fall into the previous research category, as was found for numerous related pieces of research. Nevertheless, the proposed research reflects the current digitalization progress in the field, and it can serve as a sound basis for consequent, similar studies. Furthermore, only two (although leading) databases were used—Web of Science and Scopus—and articles that were not written in the English language were excluded. On the other hand, bibliometric, content and thematic analysis provided a comprehensive overview of the current body of knowledge, and facilitated the identification of the research gaps.
The authors applied a methodological approach for the processing of publications dealing with digitalization in maritime transport and seaports. Furthermore, the aim was to answer fundamental research questions while also defining the main key points in the current maritime and seaport digitalization processes.

Author Contributions

Conceptualization, M.J., D.B., E.T. and A.P.; methodology, M.J., A.P. and D.B.; software, M.J. and A.P.; validation, D.B., A.P. and E.T.; formal analysis, M.J., D.B., E.T. and A.P.; investigation, M.J. and A.P.; writing—original draft preparation, M.J.; writing—review and editing, M.J., D.B., E.T. and A.P.; visualization, M.J., A.P. and D.B.; supervision, E.T., A.P. and D.B.; funding acquisition, A.P. and D.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Slovenian Research Agency: Program No. P5-0018—Decision Support Systems in Digital Business.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Search process in WoS and Scopus databases.
Table A1. Search process in WoS and Scopus databases.
Search StringsArticles after Applying Formal CriteriaArticles after Screening ManuallyCriteria
Web of
Science
ScopusWeb of
Science
ScopusWeb of
Science
Scopus
digitalization AND maritime transport1312102
digitalization AND maritime transportation5601
digitalization AND maritime industry162333
digitalization AND shipping485477
digitalization AND seaport131944
digitalization AND port306113
digitalisation AND maritime transport01200
digitalisation AND maritime transportation0600
digitalisation AND maritime industry32300
digitalisation AND shipping75400
digitalisation AND seaport11900
digitalisation AND port36100
ICT AND maritime transport271410
ICT AND maritime transportation101800
ICT AND maritime industry111000
ICT AND shipping707400
ICT AND seaport202400
ICT AND port27817909
Information technologies AND maritime transport9923183
Information technologies AND maritime transportation542673
Information technologies AND maritime industry17934181
Information technologies AND shipping35725391Transportation
Information technologies AND seaport514295
Information technologies AND port251105102TransportationDecision sciences, Business, Management and Accounting
Information system AND maritime transport21449111
Information system AND maritime transportation1766071
Information system AND maritime industry1773661
Information system AND shipping2777401Transportation science technology, transportationDecision sciences, Business, Management and Accounting
Information system AND seaport1987932
Information system AND port22312661 Decision sciences, Business, Management and Accounting
Port community system4874224
Information support system AND maritime transport60000
Information support system AND maritime transportation49000
Information support system AND maritime industry50000
Information support system AND shipping64000Transportation science technology, transportation
Information support system AND seaport38010
Information support system AND port41310Transportation
Decision support system AND maritime transport813550
Decision support system AND maritime transportation996102
Decision support system AND maritime industry512420
Decision support system AND shipping1267902TransportationDecision sciences, Business, Management and Accounting
Decision support system AND seaport694600
Decision support system AND port8310501Transportation science technology, transportationDecision sciences, Business, Management and Accounting
Integrated system(s) AND maritime transport172300
Integrated system(s) AND maritime transportation128710
Integrated system AND maritime industry138800
Integrated system AND shipping1946610
Integrated system(s) AND seaport14800
Integrated system(s) AND port16320710
Digital technologies AND maritime transport23410
Digital technologies AND maritime transportation7200
Digital technologies AND maritime industry36400
Digital technologies AND shipping2142110
Digital technologies AND seaport14602
Digital technologies AND port4014400
Digital data sharing AND maritime transport6020
Digital data sharing AND maritime transportation1010
Digital data sharing AND maritime industry6040
Digital data sharing AND shipping21000
Digital data sharing AND seaport1000
Digital data sharing AND port34000
Digital Maritime Traffic28000
Integrated Maritime Digital Information System15010
Maritime Single Window541130
Maritime National Single Window13361
National Maritime Single Window14400
Modern seaports811210
Seaport Integration122500
Seaport Modernization18100
Digital transformation AND maritime transport3411
Digital transformation AND maritime transportation2411
Digital transformation AND maritime industry111000
Digital transformation AND shipping272211
Digital transformation AND seaport8800
Digital transformation AND port482411
TOTAL817825518867

References

  1. Du, K.; Monios, J.; Wang, Y. Green Port Strategies in China. In Green Ports, Inland and Seaside Sustainable Transportation Strategies; Bergqvist, R., Monios, J., Eds.; Elsevier: Amsterdam, The Netherlands, 2019; pp. 211–229. [Google Scholar]
  2. Sanchez-Gonzalez, P.L.; Díaz-Gutiérrez, D.; Leo, T.J.; Núñez-Rivas, L.R. Toward Digitalization of Maritime Transport? Sensors 2019, 19, 926. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Agatić, A.; Kolanović, I. Improving the seaport service quality by implementing digital technologies. Sci. J. Marit. Res. 2020, 34, 93–101. [Google Scholar] [CrossRef]
  4. Heilig, L.; Lalla-Ruiz, E.; Voß, S. Digital transformation in maritime ports: Analysis and a game theoretic framework. NETNOMICS Econ. Res. Electron. Netw. 2017, 18, 227–254. [Google Scholar] [CrossRef]
  5. Zerbino, P.; Aloini, D.; Dulmin, R.; Mininno, V. Process-mining-enabled audit of information systems: Methodology and an application. Expert Syst. Appl. 2018, 110, 80–92. [Google Scholar] [CrossRef]
  6. Feibert, D.C.; Hansen, M.S.; Jacobsen, P. An Integrated Process and Digitalization Perspective on the Shipping Supply Chain—A Literature Review. In Proceedings of the International Conference on Industrial Engineering and Engineering Management IEEM, Singapore, 10–13 December 2017. [Google Scholar]
  7. Heering, D. Ensuring Cybersecurity in Shipping: Reference to Estonian Shipowners. Int. J. Mar. Navig. Saf. Sea Transp. 2020, 14, 271–278. [Google Scholar] [CrossRef]
  8. Kapidani, N.; Bauk, S.; Davidson, I.E. Digitalization in Developing Maritime Business Environments towards Ensuring Sustainability. Sustainability 2020, 12, 9235. [Google Scholar] [CrossRef]
  9. Vujičić, S.; Hasanspahic, N.; Car, M.; Čampara, L. Distributed Ledger Technology as a Tool for Environmental Sustainability in the Shipping Industry. J. Mar. Sci. Eng. 2020, 8, 366. [Google Scholar] [CrossRef]
  10. Pu, S.; Lam, J.S.L. Blockchain adoptions in the maritime industry: A conceptual framework. Marit. Policy Manag. 2020, 48, 777–794. [Google Scholar] [CrossRef]
  11. Gausdal, A.H.; Czachorowski, K.V.; Solesvik, M.Z. Applying Blockchain Technology: Evidence from Norwegian Companies. Sustainability 2018, 10, 1985. [Google Scholar] [CrossRef] [Green Version]
  12. Tijan, E.; Agatić, A.; Hlača, B. The Necessity of Port Community System Implementation in the Croatian Seaports. PROMET Traffic Transp. 2012, 24, 305–315. [Google Scholar] [CrossRef]
  13. Ferretti, M.; Schiavone, F. Internet of Things and business processes redesign in seaports: The case of Hamburg. Bus. Process Manag. J. 2016, 22, 271–284. [Google Scholar] [CrossRef]
  14. Aloini, D.; Benevento, E.; Stefanini, A.; Zerbino, P. Process fragmentation and port performance: Merging SNA and text mining. Int. J. Inf. Manag. 2020, 51, 101925. [Google Scholar] [CrossRef]
  15. Mraković, I.; Vojinović, R. Maritime Cyber Security Analysis—How to Reduce Threats? Trans. Marit. Sci. 2019, 8, 132–139. [Google Scholar] [CrossRef] [Green Version]
  16. Port Cybersecurity: Good Practices for Cybersecurity in the Maritime Sector. 2019. Available online: https://www.enisa.europa.eu/publications/port-cybersecurity-good-practices-for-cybersecurity-in-the-maritime-sector/at_download/fullReport (accessed on 2 February 2020).
  17. Jović, M.; Tijan, E.; Aksentijević, S.; Čišić, D. An Overview of Security Challenges of Seaport IoT Systems. In Proceedings of the 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics, Opatija, Croatia, 20–24 May 2019. [Google Scholar]
  18. Sviličić, B.; Brčić, D.; Žuškin, S.; Kalebić, D. Raising Awareness on Cyber Security of ECDIS. Int. J. Mar. Navig. Saf. Sea Transp. 2019, 13, 231–236. [Google Scholar]
  19. Meyer-Larsen, N.; Müller, R. Enhancing the Cybersecurity of Port Community Systems. In Dynamics in Logistics; Freitag, M., Kotzab, H., Pannek, J., Eds.; Springer International Publishing: Cham, Switzerland, 2018; pp. 318–323. [Google Scholar]
  20. Fruth, M.; Teuteberg, F. Digitization in maritime logistics—What is there and what is missing? Cogent Bus. Manag. 2017, 4, 1411066. [Google Scholar] [CrossRef]
  21. Bălan, C. The disruptive impact of future advanced ICTs on maritime transport: A systematic review. Supply Chain. Manag. Int. J. 2020, 25, 157–175. [Google Scholar] [CrossRef]
  22. Gil, M.; Wróbel, K.; Montewka, J.; Goerlandt, F. A bibliometric analysis and systematic review of shipboard Decision Support Systems for accident prevention. Saf. Sci. 2020, 128, 10471. [Google Scholar] [CrossRef]
  23. Tijan, E.; Jović, M.; Aksentijević, S.; Pucihar, A. Digital transformation in the maritime transport sector. Technol. Forecast. Soc. Chang. 2021, 170, 120879. [Google Scholar] [CrossRef]
  24. Yang, D.; Wu, L.; Wang, S.; Jia, H.; Li, K.X. How big data enriches maritime research—A critical review of Automatic Identification System (AIS) data applications. Transp. Rev. 2019, 39, 755–773. [Google Scholar] [CrossRef]
  25. Dreyer, S.; Olivotti, D.; Lebek, B.; Breitner, M.H. Focusing the customer through smart services: A literature review. Electron. Mark. 2019, 29, 55–78. [Google Scholar] [CrossRef] [Green Version]
  26. Guo, Y.-M.; Huang, Z.L.; Guo, J.; Guo, X.R.; Li, H.; Liu, M.-Y.; Ezzedine, S.; Nkeli, M.J. A bibliometric analysis and visualization of blockchain. Future Gener. Comput. Syst. 2021, 116, 316–332. [Google Scholar] [CrossRef]
  27. Vidmar, D.; Marolt, M.; Pucihar, A. Information Technology for Business Sustainability: A Literature Review with Automated Content Analysis. Sustainability 2021, 13, 1192. [Google Scholar] [CrossRef]
  28. Myllärniemi, V. Quality Attribute Variability in Software Product Lines-Varying Performance and Security Purposefully. Empirical Software Engineering. 2015. Available online: https://www.semanticscholar.org/paper/Quality-Attribute-Variability-in-Software-Product-Myllärniemi/11f3186ef836fc380b23b765bb30fd6873f3d556 (accessed on 15 February 2022).
  29. Hirata, E. Service characteristics and customer satisfaction in the container liner shipping industry. Asian J. Shipp. Logist. 2019, 35, 24–29. [Google Scholar] [CrossRef]
  30. Baccelli, O.; Morino, P. The role of port authorities in the promotion of logistics integration between ports and the railway system: The Italian experience. Res. Transp. Bus. Manag. 2020, 35, 100451. [Google Scholar] [CrossRef]
  31. Chen, C. Visualizing Patterns and Trends in Scientific Literature. Available online: http://cluster.cis.drexel.edu/~cchen/citespace (accessed on 18 September 2021).
  32. Kao, S.-L.; Lee, K.-T.; Chang, K.-Y.; Ko, M.-D. A fuzzy logic method for collision avoidance in Vessel Traffic Service. J. Navig. 2007, 60, 17–31. [Google Scholar] [CrossRef]
  33. Yang, C.S. Maritime shipping digitalization: Blockchain-based technology applications, future improvements, and intention to use. Transp. Res. Part E Logist. Transp. Rev. 2019, 131, 108–117. [Google Scholar] [CrossRef]
  34. Kia, M.; Shayan, E.; Ghotb, F. The importance of information technology in port terminal operations. Int. J. Phys. Distrib. Logist. Manag. 2000, 30, 331–344. [Google Scholar] [CrossRef] [Green Version]
  35. Zerbino, P.; Aloini, D.; Dulmin, R.; Mininno, V. Towards Analytics-Enabled Efficiency Improvements in Maritime Transportation: A Case Study in a Mediterranean Port. Sustainability 2019, 11, 4473. [Google Scholar] [CrossRef] [Green Version]
  36. Di Vaio, A.; Varriale, L. AIS and Reporting in the Port Community Systems: An Italian Case Study in the Landlord Port Model. In Reshaping Accounting and Management Control Systems; Corsi, K., Castellano, N.G., Lamboglia, R., Mancini, D., Eds.; Springer International Publishing: Cham, Switzerland, 2017; pp. 153–165. [Google Scholar]
  37. Ellingsen, O.; Aasland, K.E. Digitalizing the maritime industry: A case study of technology acquisition and enabling advanced manufacturing technology. J. Eng. Technol. Manag. 2019, 54, 12–27. [Google Scholar] [CrossRef]
  38. Sviličić, B.; Kamahara, J.; Čelić, J.; Bolmsten, J. Assessing ship cyber risks: A framework and case study of ECDIS security. WMU J. Marit. Aff. 2019, 18, 509–520. [Google Scholar] [CrossRef]
  39. Zhao, S.; Liang, W.; Han, D. Seaport Logistics Information Sharing Platform in E-commerce: A Case Study of QDIP in China. In Proceedings of the 3rd IEEE and IFIP South Central Asian Himalayas Regional International Conference on Internet (AH-ICI), Kathmandu, Nepal, 23–25 November 2012. [Google Scholar]
  40. Moros-Daza, A.; Amaya-Mier, R.; Garcia-Llinas, G.; Voß, S. Port Community System Adoption: Game Theoretic Framework for an Emerging Economy Case Study. In Computational Logistics; Paternina-Arboleda, C., Voß, S., Eds.; Springer International Publishing: Cham, Switzerland, 2019; pp. 136–153. [Google Scholar]
  41. Balci, G. Digitalization in Container Shipping Services: Critical Resources for Competitive Advantage. J. ETA Marit. Sci. 2021, 9, 3–12. [Google Scholar] [CrossRef]
  42. Del Giudice, M.; Di Vaio, A.; Hassan, R.; Palladino, R. Digitalization and new technologies for sustainable business models at the ship-port interface: A bibliometric analysis. Marit. Policy Manag. 2021, 1–37. [Google Scholar] [CrossRef]
  43. Zeng, F.; Chan, H.K.; Pawar, K. The effects of inter- and intraorganizational factors on the adoption of electronic booking systems in the maritime supply chain. Int. J. Prod. Econ. 2021, 236, 108119. [Google Scholar] [CrossRef]
  44. Wang, S.; Zhen, L.; Xiao, L.; Attard, M. Data-Driven Intelligent Port Management Based on Blockchain. Asia-Pac. J. Oper. Res. 2021, 38, 2040017. [Google Scholar] [CrossRef]
  45. Jović, M.; Tijan, E.; Žgaljić, D.; Aksentijević, S. Improving Maritime Transport Sustainability Using Blockchain-Based Information Exchange. Sustainability 2020, 12, 8866. [Google Scholar] [CrossRef]
  46. Tsiulin, S.; Reinau, K.H.; Hilmola, O.-P.; Goryaev, N.; Karam, A. Blockchain-based applications in shipping and port management: A literature review towards defining key conceptual frameworks. Rev. Int. Bus. Strategy 2020, 30, 201–224. [Google Scholar] [CrossRef]
  47. Jugović, A.; Bukša, J.; Dragoslavić, A.; Sopta, D. The Possibilities of Applying Blockchain Technology in Shipping. Sci. J. Marit. Res. 2019, 33, 274–279. [Google Scholar] [CrossRef]
  48. Todd, P. Electronic bills of lading, blockchains and smart contracts. Int. J. Law Inf. Technol. 2019, 27, 339–371. [Google Scholar] [CrossRef]
  49. Henesey, L.; Lizneva, Y.; Philipp, R.; Meyer, C.; Gerlitz, L. Improved load planning of RoRo Vessels by adopting Blockchain and Internet-of-Things. In Proceedings of the 22nd International Conference on Harbor, Maritime and Multimodal Logistics Modelling and Simulation, Online, 16–18 September 2020. [Google Scholar]
  50. Irannezhad, E.; Faroqi, H. Addressing some of bill of lading issues using the Internet of Things and blockchain technologies: A digitalized conceptual framework. Marit. Policy Manag. 2021, 1–19. [Google Scholar] [CrossRef]
  51. Liu, J.; Zhang, H.; Zhen, L. Blockchain technology in maritime supply chains: Applications, architecture and challenges. Int. J. Prod. Res. 2021, 1–17. [Google Scholar] [CrossRef]
  52. Poulis, E.; Poulis, K.; Dooley, L. Information communication technology’ innovation in a non-high technology sector: Achieving competitive advantage in the shipping industry. Serv. Ind. J. 2013, 33, 594–608. [Google Scholar] [CrossRef]
  53. Jović, M.; Kavran, N.; Aksentijević, S.; Tijan, E. The Transition of Croatian Seaports into Smart Ports. In Proceedings of the 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia, 20–24 May 2019. [Google Scholar]
  54. Di Vaio, A.; Varriale, L. Digitalization in the sea-land supply chain: Experiences from Italy in rethinking the port operations within inter-organizational relationships. Prod. Plan. Control 2020, 31, 220–232. [Google Scholar] [CrossRef]
  55. Bui, V.D.; Nguyen, H.P. A Comprehensive Review on Big Data-Based Potential Applications in Marine Shipping Management. Int. J. Adv. Sci. Eng. Inf. Technol. 2021, 11, 1067–1077. [Google Scholar] [CrossRef]
  56. John, A.; Yang, Z.; Riahi, R.; Wang, J. A Decision Support System for the Assessment of Seaports’ Security Under Fuzzy Environment. In Intelligent Systems Reference Library; Springer: Cham, Switzerland, 2018; pp. 145–177. [Google Scholar]
  57. Nota, G.; Bisogno, M.; Saccomanno, A. A service-oriented approach to modeling and performance analysis of Port Community Systems. Int. J. Eng. Bus. Manag. 2018, 10, 1847979018767766. [Google Scholar] [CrossRef] [Green Version]
  58. Irannezhad, E.; Hickman, M.; Prato, C.G. Modeling the Efficiency of a Port Community System as an Agent-based Process. Procedia Comput. Sci. 2017, 109, 917–922. [Google Scholar] [CrossRef]
  59. Maeder, C.; Sohr, K.; Nguempnang, R.W.; Meyer-Larsen, N.; Müller, R. Modeling and Validating Role-Based Authorization Policies for a Port Communication System with UML and OCL. J. Object Technol. 2020, 19, 1–14. [Google Scholar] [CrossRef]
  60. Carlan, V.; Sys, C.; Vanelslander, T.; Roumboutsos, A. Digital innovation in the port sector: Barriers and facilitators. Compet. Regul. Netw. Ind. 2017, 18, 71–93. [Google Scholar] [CrossRef]
  61. Tijan, E.; Agatić, A.; Hlača, B. ICT evolution in container terminals. Sci. J. Marit. Res. 2010, 24, 27–40. [Google Scholar]
  62. Yanchin, I.; Petrov, O. Towards Autonomous Shipping: Benefits and Challenges in the Field of Information Technology and Telecommunication. Int. J. Mar. Navig. Saf. Sea Transp. 2020, 14, 611–619. [Google Scholar] [CrossRef]
  63. Jing, M.; Zheng, W. Research and Application of Real-time Ship Traffic Intelligent Analysis and Calculation Platform under 5G Background. In IOP Conference Series-Earth and Environmental Science; IOP Publishing: Bristol, UK, 2019. [Google Scholar]
  64. Kim, S.; Kim, H.; Park, Y. Early detection of vessel delays using combined historical and real-time information. J. Oper. Res. Soc. 2017, 68, 182–191. [Google Scholar] [CrossRef]
  65. Islam, S.; Goerlandt, F.; Feng, X.; Uddin, M.J.; Shi, Y.; Hilliard, C. Improving disasters preparedness and response for coastal communities using AIS ship tracking data. Int. J. Disaster Risk Reduct. 2020, 51, 101863. [Google Scholar] [CrossRef]
  66. Yan, Z.; Xiao, Y.; Cheng, L.; He, R.; Ruan, X.; Zhou, X.; Li, M.; Bin, R. Exploring AIS data for intelligent maritime routes extraction. Appl. Ocean Res. 2020, 101, 10227. [Google Scholar] [CrossRef]
  67. Rajabi, A.; Saryazdi, A.K.; Belfkih, A.; Duvallet, C. Towards Smart Port: An Application of AIS Data. In Proceedings of the IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems, Exeter, UK, 28–30 June 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 1414–1421. [Google Scholar]
  68. Marek, R. A Qualitative Analysis of Using Swibż System into Creation of Polish Port Community System. In Proceedings of the Carpathian logistics congress (CLC’ 2016), Zakopane, Poland, 28–30 November 2016. [Google Scholar]
  69. Alop, A. The Main Challenges and Barriers to the Successful Smart Shipping. Int. J. Mar. Navig. Saf. Sea Transp. 2019, 13, 521–528. [Google Scholar] [CrossRef]
  70. Keceli, Y. A proposed innovation strategy for Turkish port administration policy via information technology. Marit. Policy Manag. 2011, 38, 151–167. [Google Scholar] [CrossRef]
  71. Carlan, V.; Sys, C.; Vanelslander, T. How port community systems can contribute to port competitiveness: Developing a cost-benefit framework. Res. Transp. Bus. Manag. 2016, 19, 51–64. [Google Scholar] [CrossRef]
  72. Moros-Daza, A.; Amaya-Mier, R.; Paternina-Arboleda, C. Port Community Systems: A structured literature review. Transp. Res. Part A Policy Pract. 2020, 133, 27–46. [Google Scholar] [CrossRef]
  73. Tucci, A.E. Cyber Risks in the Marine Transportation System. In Cyber-Physical Security: Protecting Critical Infrastructure at the State and Local Level; Springer: Cham, Switzerland, 2017; pp. 113–131. [Google Scholar]
  74. Parola, F.; Satta, G.; Buratti, N.; Vitellaro, F. Digital technologies and business opportunities for logistics centres in maritime supply chains. Marit. Policy Manag. 2020, 48, 461–477. [Google Scholar] [CrossRef]
  75. Karaś, A. Smart Port as a Key to the Future Development of Modern Ports. TransNav Int. J. Mar. Navig. Saf. Sea Transp. 2020, 14, 27–31. [Google Scholar] [CrossRef]
  76. Dominguez-Péry, C.; Vuddaraju, L.N.R. From Human Automation Interactions to Social Human Autonomy Machine Teaming in Maritime Transportation. In IFIP Advances in Information and Communication Technology; Springer: Cham, Switzerland, 2020; pp. 45–56. [Google Scholar]
  77. Ray, C.; Goralski, R.; Claramunt, C.; Gold, C. Real-Time 3D Monitoring of Marine Navigation. In Lecture Notes in Geoinformation and Cartography; Springer: Cham, Switzerland, 2011; pp. 161–175. [Google Scholar]
  78. Lebbadi, T. Role of the Institutional Theory for Implementation Information Technology to Enhance Safety Management in Shipping Companies. In Proceedings of the 2015 Science and Information Conference, London, UK, 28–30 July 2015; pp. 1340–1351. [Google Scholar]
  79. Komorčec, D.; Matika, D. Small crafts role in maritime traffic and detection by technology integration. Sci. J. Marit. Res. 2016, 30, 3–11. [Google Scholar] [CrossRef] [Green Version]
  80. Xiao, Z.; Fu, X.; Zhang, L.; Goh, R.S.M. Traffic Pattern Mining and Forecasting Technologies in Maritime Traffic Service Networks: A Comprehensive Survey. IEEE Trans. Intell. Transp. Syst. 2020, 21, 1796–1825. [Google Scholar] [CrossRef]
  81. Miler, R.K.; Bujak, A. Exact Earth Satellite—AIS as One of the Most Advanced Shipping Monitoring Systems. In Proceedings of the 13th International Conference on Transport Systems Telematics, Katowice-Ustron, Poland, 23–26 October 2013; pp. 330–337. [Google Scholar]
  82. Hult, C.; Praetorius, G.; Sandberg, C. On the Future of Maritime Transport—Discussing Terminology and Timeframes. Int. J. Mar. Navig. Saf. Sea Transp. 2019, 13, 269–273. [Google Scholar] [CrossRef]
  83. Heilig, L.; Voß, S. Information systems in seaports: A categorization and overview. Inf. Technol. Manag. 2017, 18, 179–201. [Google Scholar] [CrossRef]
  84. Han, P.; Yang, X. Big data-driven automatic generation of ship route planning in complex maritime environments. Acta Oceanol. Sin. 2020, 39, 113–120. [Google Scholar] [CrossRef]
  85. European Commission. A European approach to artificial intelligence. Available online: https://digital-strategy.ec.europa.eu/en/policies/european-approach-artificial-intelligence (accessed on 2 February 2022).
  86. Cristea, D.S.; Moga, L.M.; Neculita, M.; Prentkovskis, O.; Nor, K.M.; Mardani, A. Operational shipping intelligence through distributed cloud computing. J. Bus. Econ. Manag. 2017, 18, 695–725. [Google Scholar] [CrossRef] [Green Version]
  87. Hansa International Maritime Journal. Case for Port Automation Not Entirely Convincing. Available online: https://hansa-online.de/2021/11/haefen/184486/case-for-port-automation-not-entirely-convincing (accessed on 15 December 2021).
Figure 1. Research steps.
Figure 1. Research steps.
Jmse 10 00486 g001
Figure 2. Paper publications per year.
Figure 2. Paper publications per year.
Jmse 10 00486 g002
Figure 3. The visualization of the “country cooperation network”.
Figure 3. The visualization of the “country cooperation network”.
Jmse 10 00486 g003
Figure 4. Distribution of the articles by their classification.
Figure 4. Distribution of the articles by their classification.
Jmse 10 00486 g004
Figure 5. Categories based on the publications.
Figure 5. Categories based on the publications.
Jmse 10 00486 g005
Figure 6. Identified themes and their distribution in the analyzed publications.
Figure 6. Identified themes and their distribution in the analyzed publications.
Jmse 10 00486 g006
Figure 7. Leximancer concept map.
Figure 7. Leximancer concept map.
Jmse 10 00486 g007
Table 1. Inclusion and exclusion criteria.
Table 1. Inclusion and exclusion criteria.
Inclusion CriteriaExclusion Criteria
Topic or Title in WOS; Article title, Abstract, and Keywords in ScopusPapers in which digitalization is both mentioned to a small extent, and is not a main focus
Fields: ICT and other solutions/systems for port operation planning, berth allocation, human resources planning, decision making, routing optimization, information exchange, e-navigation and cybersecurityPapers on shipbuilding industry and ship design
Categories for general keywords: “Transportation” and “Transport Science Technology”, “Decision Sciences” and “Business, Management and Accounting”Papers that explicitly referred to surveillance
No limitation to high-ranking periodical publicationsNon-English language
Type of papers: journals, conference papers, and book chapters
Table 2. Top 10 countries based on publications.
Table 2. Top 10 countries based on publications.
No.CountCentralityCountry
1400.07Croatia
2250.77Germany
3230.37England
4230.57Italy
5220.42People’s Republic of China
6170.30USA
7160.45Norway
8150.15Spain
9150.14Poland
10140.43Sweden
Table 3. Types of papers.
Table 3. Types of papers.
No.Type of PaperCount%
1Journal17662.86%
2Conference9433.57%
3Book chapter103.57%
Table 4. Top countries by the number of conference papers.
Table 4. Top countries by the number of conference papers.
No.CountCountry%
111Poland11.70%
28Croatia8.51%
36Russia6.38%
46Greece6.38%
55USA5.32%
65Italy5.32%
Table 5. Top five countries according to their number of journal papers.
Table 5. Top five countries according to their number of journal papers.
No.CountCountry%
162England35.23%
221Croatia11.93%
320Netherlands11.36%
418USA10.23%
517Poland9.66%
Table 6. Top five journals according to the number of published papers.
Table 6. Top five journals according to the number of published papers.
No.JournalImpact Factor (2020)CountryCount% Of Papers
JCRSJR
1TransNav: International Journal on Marine Navigation and Safety of Sea Transportation-0.25Poland147.95%
2Scientific Journal of Maritime Research-0.197Croatia137.39%
3Maritime Policy & Management3.7781.046United Kingdom105.68%
4Sustainability3.2510.612Switzerland73.98%
5WMU Journal of Maritime Affairs-0.585Germany52.84%
Table 7. Most cited papers and authors according to the WOS database (9 February 2022).
Table 7. Most cited papers and authors according to the WOS database (9 February 2022).
No.PaperAuthorsTotal Citations
1A fuzzy logic method for collision avoidance in Vessel Traffic ServiceS.-L. Kao, K.-T. Lee, K.-Y. Chang, and M.-D. Ko95
2Maritime shipping digitalization: Blockchain-based technology applications, future improvements, and intention to useC. S. Yang66
3How big data enriches maritime research—a critical review of Automatic Identification System (AIS) data applicationsD. Yang, L. Wu, S. Wang, H. Jia, and K. X. Li55
4How port community systems can contribute to port competitiveness: Developing a cost-benefit frameworkV. Carlan, C. Sys, and T. Vanelslander52
5Internet of Things and Business Processes Redesign in Seaports: The case of HamburgM. Ferretti and F. Schiavone47
Table 8. Most cited papers and authors according to the Scopus database (9 February 2022).
Table 8. Most cited papers and authors according to the Scopus database (9 February 2022).
No.PaperAuthorsTotal Citations
1A fuzzy logic method for collision avoidance in Vessel Traffic ServiceS.-L. Kao, K.-T. Lee, K.-Y. Chang, and M.-D. Ko101
2The importance of information technology in port terminal operationM. Kia, E. Shayan, and F. Ghotb80
3Maritime shipping digitalization: Blockchain-based technology applications, future improvements, and intention to useC. S. Yang72
4How port community systems can contribute to port competitiveness: Developing a cost-benefit frameworkV. Carlan, C. Sys, and T. Vanelslander63
5Internet of Things and Business Processes Redesign in Seaports: The case of HamburgM. Ferretti and F. Schiavone62
Table 9. Top five categories based on publications.
Table 9. Top five categories based on publications.
No.Subject CategoryCount
1Transportation84
2Engineering73
3Computer Science52
4Business and Economics43
5Transportation Science & Technology39
Table 10. Top 10 keywords by frequency (Author Keywords and Keywords Plus).
Table 10. Top 10 keywords by frequency (Author Keywords and Keywords Plus).
No.KeywordsCountNo.KeywordsCount
1Port (Seaport, Harbour)416Internet of Things19
2Port Community System (PCS)277Model18
3Big Data268Management17
4Information System229Information technology; System; Blockchain16
5Supply chain; Digitalization2110Ship; Logistics; Technology; Smart port; Artificial Intelligence15
Table 11. Future research directions.
Table 11. Future research directions.
TermAlready Analyzed TopicsFuture Research Directions
AIS (Automatic Identification System)A data-driven method for detecting delays of vessels, considering stages, using historical shipping data and real-time S-AIS vessel tracking data [2,64]Inclusion of other various types of data such as weather and social data [64]
AIS (Automatic Identification System)Developed concept of using AIS data in a disaster relief operation [65]AIS for planning and supporting humanitarian relief operations [65]
AIS (Automatic Identification System)The proposition of AIS-based maritime route extraction method for realization of vessels’ traffic routes extraction [66]Consideration of vessels’ type influence in function of improvement of the proposed method, along with usual parameters (water depth, distance from coastline, and number of vessel navigation characteristic area and points, e.g. stop- and waypoint areas, ship stop points, etc.) [66]
AIS (Automatic Identification System)AIS analyses which could be the use cases and services of a smart port [67]Possible use of AIS data for computing CO2 emissions of vessels, proposing berth allocation problems, and improving prediction and modeling of vessel trajectories [67]
goodsThe impact of PCS in terms of managing goods in sea-land supply chain or port supply chain [54,68], the advantages of Smart contracts (the automatic change of ownership of goods) [45]; The role of Blockchain regarding goods transportation records [46]The application of new digital technologies for the processing of goods in ports and sea-land supply chain [54,69]
innovationInnovation strategy for Turkish port for successful development of a port community system, using SWOT analysis [70]Guideline for policy makers regarding innovations in maritime transport and seaports
innovationDigital innovation in the port
(barriers and facilitators) [60]
How to improve the cooperation among stakeholders in order to support the successful adoption of innovation [60,71]
innovationNot applicable because no direct link has been found between the terms “innovation” and “PCS” from which a conclusion could be drawnA lack of research studies regarding PCS innovations, which risks relegating PCS research to irrelevance [72]
innovationNot applicable because no direct link has been found between the terms “innovation” and “cyber risks” from which a conclusion could be drawn Methods for mitigating cyber risks which are increasing with the development of new digital technologies (innovations) [73]
innovationInvestigation of how the adoption of emerging digital technologies (innovations) can provide valuable business opportunities for logistics centres in maritime supply chains [74]Innovations such as IoT, Blockchain, Mobile devices are still insufficiently researched or neglected [74]
innovationAnalyzed projects introduced in the ports of the North Sea and Baltic Sea which are successively implementing the concept of a Smart Port [75]Which technologies to choose, how to implement them remains a challenge; in the context of smart ports [75]
innovationDiscussed conceptual evolution from Human-Automation Interaction to Human Autonomy Teaming and presented the risks of high levels of automation and the importance of teamwork in safety critical systems [76]Recent technological advances (innovations) in the field of Artificial intelligence which can increasingly affect maritime transport sector [76]
innovationIntroduced a three-dimensional (3D) GIS applied to maritime navigation [77]The range of innovations required to reach real-time 3D monitoring of marine navigation [77]
Innovation and organizationsNot applicable because no direct link has been found between the terms “innovation” and “organizations” from which a conclusion could be drawn What is necessary to mitigate diversity of attitudes towards adoption of innovations by people (it has sometimes been argued to be the outcome of some cultural resistance to new products and technologies—a factor that may hinder widespread adoption) [78]
OrganizationsDrivers, success factors and barriers of digital transformation in organizations [23]Further studies necessary to gain deeper insights into how to design successful digital transformation [23,78]
surveillanceThe importance of maritime surveillance for safety and security of sea traffic, using different technologies (e.g., satellite technology) [8,79]; the information systems and the corresponding traffic or sensor data collected for ship tracking, monitoring and maritime traffic surveillance [80], e.g., AIS [65,66,81], coastal radar etc.What kind of IT-infrastructure is necessary to enable shore-based traffic monitoring as complement to current surveillance and monitoring services, such as VTS [82]; for the purpose of more qualitative and comprehensive maritime traffic surveillance [79]
surveillanceThe importance of surveillance cameras in seaports [17]The possibilities of smart video surveillance systems [83]
E-booking systemsExplored the use of e-booking systems in the maritime supply chain and, in particular, the factors influencing the adoption of such systems at the organizational level (multi-case study of eight firms across multiple tiers of the maritime supply chain) [43]For more detail research, ownership structure and firm size, which might affect the adoption of information system can be included in the research [43]
Ship route planningProposed method specialized in generating optimal ship planning routes for a timely maritime emergency search and rescue, reducing the execution time for maritime search and rescue services in practical applications [84]Further investigation of possibilities of suggested method, that may provide a tremendous amount of information for maritime emergency search and rescue without the assistance of the widely-used electronic chart [84]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Jović, M.; Tijan, E.; Brčić, D.; Pucihar, A. Digitalization in Maritime Transport and Seaports: Bibliometric, Content and Thematic Analysis. J. Mar. Sci. Eng. 2022, 10, 486. https://doi.org/10.3390/jmse10040486

AMA Style

Jović M, Tijan E, Brčić D, Pucihar A. Digitalization in Maritime Transport and Seaports: Bibliometric, Content and Thematic Analysis. Journal of Marine Science and Engineering. 2022; 10(4):486. https://doi.org/10.3390/jmse10040486

Chicago/Turabian Style

Jović, Marija, Edvard Tijan, David Brčić, and Andreja Pucihar. 2022. "Digitalization in Maritime Transport and Seaports: Bibliometric, Content and Thematic Analysis" Journal of Marine Science and Engineering 10, no. 4: 486. https://doi.org/10.3390/jmse10040486

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop