Abstract
Introduction
A crucial part of regional and international trade is played by the maritime logistics sector. A study shows that more than 80% of global trade in terms of volume is carried out over the oceans (Ducruet & Guerrero, 2022). But in recent years, the marine economy sector has encountered numerous significant difficulties, including high fragmentation, low transparency and visibility, expensive manual processes, frequently out-of-date customer interfaces, volatile fuel prices, demand uncertainties, environmental regulations, and fierce competition from other freight transport modes as well as within the sector (Raza et al., 2019, 2023; Rodrigue et al., 2017). Additionally, manual procedures and a deficiency in seamless coordination among various marine logistics players cause longer transit times, delays, poor dependability, and a rise in the price of maritime logistics services (Jensen et al., 2018; Panayides & Song, 2013). More specifically, the COVID-19 epidemic has had a significant impact on the procedures and operational practices of marine businesses. Most shipping lines and logistics firms must conduct business online in 2020 due to the closure of several of their offices. Furthermore, in order to address climate change’s rising threat, governments around the world have prioritized carbon reduction and neutrality. These goals emphasize taking action on climate change to mitigate the negative consequences of human activity and using fossil fuels for energy (Bekun, 2024). To overcome the aforementioned restrictions, the role of digitalization for marine logistics operations has recently developed as the applied discourse of maritime informatics (Lind et al., 2021b). Maritime informatics is considered as a thematic discussion within the larger field of informatics, where digital transformation is mainly focused on the use of information systems, data analytics, and sharing to enhance the efficiency, competence, safety, resilience, and ecological sustainability of the global maritime industry (Lind et al., 2021; Lind, Bergmann et al., 2018; Otuaga et al., 2023).
Through digital transformation (DT), which is defined as the necessary transformations driving digitalization within and between organizations based on a digital strategy, there are significant opportunities to improve the environmental and financial performance of stakeholders involved in maritime logistics (Heilig et al., 2017; Kache & Seuring, 2017; Lind, Watson et al., 2018). Entry barriers have been lowered in many nations due to the development of digital technology, current tendencies toward globalization, and the opening of international boundaries, allowing new entrants into a market that is already extremely competitive on a global scale (Gefen & Carmel, 2008). Furthermore, DT empowers businesses to increase flexibility and efficiency, streamline manufacturing processes, create value propositions for innovation ecosystems, and promptly adapt to market demands (Alcácer & Cruz-Machado, 2019; Chen et al., 2018; Queiroz et al., 2020). Additionally, DT procedures are crucial for preserving market competitiveness and being at the forefront of technological innovation. Even though it is anticipated that DT would have a significant impact on the maritime sector, seaport stakeholders and businesses struggle with a lack of understanding, a lack of desire, and the right strategies and initiatives to apply for effective DT (Gausdal et al., 2018).
According to Oh et al. (2022), due to the accelerating DT, 75% of Fortune’s top 500 global corporations are predicted to push organizational changes through new technologies by 2025. Companies that do not adapt to technological changes in paradigms will fail, while only those that evolve will survive. However, DT’s success rate is anticipated to be as low as 30% (Siebel, 2017). Companies acknowledge the importance of DT, but execution remains a challenge (Gupta, 2018; Morgan, 2019). The adoption of DT in shipping and logistics is hampered by a number of major reasons, including lack of a DT strategy, inadequate technical knowledge and skills, high cost, lack of standardization and interoperability, and cyber security (Egloff et al., 2018; Gunasekaran et al., 2017; Mathauer & Hofmann, 2019; Pagano et al., 2022; Tijan et al., 2021). The necessity for more study to examine obstacles to data transfer (DT) in maritime logistics has been highlighted by recent literature evaluations conducted by Munim et al. (2020) and Parola et al. (2021). To overcome digital transformation challenges, firms must understand the motivations behind digital technology adoption. Accordingly, identifying positive performances from digital transformation might boost corporate motivation (Bharadwaj et al., 2013). Furthermore, recognizing factors that encourage digital transformation, such as government regulations and competition from other enterprises, may contribute to faster digital transformation in the maritime sector (Nguyen and Luu, 2020). However, empirical research on this topic in the marine industry remains limited (Goggin, 2021). As a result, this study has two primary aims. The first goal is to investigate digital transformation activities to improve economic and environmental performances in maritime transport enterprises, particularly in light of the need for these enterprises to actively apply technology to reduce emissions in accordance with regulations (UNCTAD, 2022). The second goal is to identify key factors in digital transformation, allowing managers to develop effective implementation strategies (Kraus et al., 2021).
The current study is organized as follows: in Section 2, we supply a literature review for this topic and develop the research hypotheses. Section 3 describes the data and research approach. Section 4 exhibits the outcomes of the data analysis, while Section 5 discusses the results, the theory and practical implications, limitations and future research orientation of the research. Finally, Section 6 presents the conclusions of this research.
Literature Review
The Technology-Organization-Environment (TOE) Framework
The Technology-Organization-Environment (TOE) framework represents the concept that digital technologies should be studied by focusing on the organization as a whole (Nikopoulou et al., 2023). To put it another way, the TOE framework is an organization-level theory that explains how three different parts of a firm’s context, namely the technical, organizational, and environmental context, impact adoption decisions (Tornatzky et al., 1990). Specifically, the inclusion of these factors establishes TOE as the most appropriate theory for examining technological adaptation, technology utilization, and the development of value from technological innovations (Cruz-Jesus et al., 2019).
Technology is the initial context, and it acts as a lens through which to view the internal and external technical factors that an organization has to address (Oliveira & Martins, 2011). Complexity, availability, scalability, cost, and security are some of these technical qualities that have historically been present (Ngah et al., 2017). The framework also includes a suggested organizational environment that offers descriptive metrics of the organization such as scope, firm size, technical skills, organizational preparation, and resources (Ngah et al., 2017). The third context, environment, relates to the business or industry in which the organization now operates. This environment can have an impact on the company’s operations, including the adoption and deployment of technology. According to Daniels and Jokonya (2020) and Ngah et al. (2017), market determinants include industry competitors, governmental pressures, market structure, and supplier capabilities. Competition pressure and regulations from governments will be examined in this research.
According to Daniels and Jokonya (2020) and Oliveira and Martins (2011), the TOE framework serves as one of the key basis for researchers and managers when assessing, evaluating, and implementing technology inside an organization. First of all, the TOE framework offers researchers a strong theoretical grounding and explains the effects of many factor groups on technological activities for businesses (Daniels & Jokonya, 2020). Second, when planning to apply and integrate technology into the company model, managers often utilize the TOE framework as one of the crucial tools (Oliveira & Martins, 2011) to assess the advantages and possible dangers.
Recent studies have used the TOE framework to understand the variables that influence the technology adoption process in the maritime industry. For example, Lin (2024) discovered that knowledge absorption capability is the most important enabler of blockchain adoption in the organizational context, followed by perceived relative advantage in the technological context and trading partner influence in the environmental context, using the TOE framework to explain blockchain adoption in the marine industry. At the same time, X. Li et al. (2024) found that the top five requirements for blockchain deployment are relative advantage, internal leadership, human resource competency, scalability, and ease of use. Zeng et al. (2020) used the generic TOE framework as the theoretical basis for their study, the adoption of inter-organizational information systems in the maritime supply chain was found to be influenced by a number of significant factors, including industrial characteristics, information confidentiality, supply chain partners’ power, government power, and ownership structure, in addition to the factors found in previous literature, such as relative advantage, ease of use, firm size, and top management support.
The TOE framework is employed in this study to investigate and put into practice activities related to DT for maritime enterprises. Managers at shipping firms will have a solid foundation for their DT decision-making through the evaluation and testing of the influence of technology, organization, and environment factors using the TOE framework.
Digital Transformation in Shipping
The change of sociotechnical systems via the application of digital technology and digitalized data is referred to as digitalization (Raza et al., 2023). According to Alcácer and Cruz-Machado (2019), “Industry 4.0 ushers in the digitalization era,” which has an influence on commercial operations and enables enhanced and automated supply chain procedures as well as increased business cooperation. The digital revolution and platforms have revolutionized businesses and created enormous potential for entrepreneurs (Feliciano-Cestero et al., 2023; Nambisan et al., 2019; Sturgeon, 2021). Kohtamäki et al. (2021) assert that digital technologies and tactics facilitate the rapid introduction of novel products and services that can cross international borders and have a substantial impact on the institutional framework and economic activity of both home and host countries (Feliciano-Cestero et al., 2023; Patrucco et al., 2020; Stallkamp & Schotter, 2019). Additionally, they have the potential to influence the internationalization process in terms of its duration, speed, geographical aspects, entry method, assimilation and integration of foreign markets, as well as the accessibility to local market resources and capabilities (Coviello et al., 2017; Vadana et al., 2021). Moreover, by embracing “digitization, advanced data science, and business intelligence techniques” (Lederer & Riedl, 2020), businesses opting to operate on a local or global scale will enable the success of knowledge-intensive services and processes. The implementation of DT not only revitalizes the industry but also facilitates the adoption of emerging technologies, altering lifestyles, generating novel business models, and revolutionizing manufacturing approaches (Alcácer & Cruz-Machado, 2019). This transition toward DT significantly reshapes business models, leading to a reimagining of interactions among consumers, businesses, and suppliers.
According to PwC Norway (2017), which surveyed 28 ocean shipping decision-makers, the DT is expected to play an important role in shipping and for shipping enterprises in the future, and the marine sector is currently confidently anticipating major digitization operations. The good news is that the shipping industry uses a wide variety of equipment and gear. The efficiency of the seaport would considerably rise if these machines began talking with one another, acquiring and analyzing data, and making decisions in real-time without human intervention (Donepudi, 2014).
The state-of-the-art digitalization in maritime transport was verified by Sanchez-Gonzalez et al. (2019), who also noted that digitalization currently applies to eight digital domains: “autonomous vehicles and robotics; artificial intelligence; big data; virtual reality, augmented reality, and mixed reality; internet of things; the cloud and edge computing; digital security; and 3D printing and additive engineering.” By utilizing the cloud’s infrastructure, ports may concentrate on their core competencies while gaining processing speed, cost effectiveness, security, high availability, and resilience. The IoT technologies make it possible for the gadgets to develop intelligence, communicate with other gadgets, and gather and share information about their operational state and status (Figure 1).
Cloud platform: high availability and continuous communication (Donepudi, 2014).
The Context of the Vietnam
Through the Prime Minister’s Decision No. 749/QD-TTg, “National Digital Transformation Program to 2025, with orientation to 2030,” the Government of Vietnam affirmed its resolve to digitally change the economy. The program’s objective is to create a digital economy and government in order to improve the operational effectiveness of the social and economic sectors (Government, 2020). Action plans for the DT have been released by ministries in turn.
In the maritime sector, Vietnam National Shipping Lines Corporation - Joint Stock Company (VIMC), the organization responsible for overseeing the nation’s extensive network of ports, has actively implemented DT and applied technology to boost the operational effectiveness of the port (Iệp, 2023). Two crucial solutions for the digital transition have been formally implemented by VIMC. The first is the VIMC-Working Place office operating system, which manages all transactions, work, official documents, cloud storage, administrative processes, and office expenditures for each person. operates on any platform, is private, and makes use of digital signatures. The second is the Logistics Hub System, which uses robotics and AI (artificial intelligence) technology. The following client will only be able to contact VIMC through the Logistics Hub. By using this technology, member companies may also provide customer service, transforming VIMC from an entity with favorable infrastructure into a key player in the online market (Tienphong, 2023).
Following a general direction in the marine industry, Vietnamese seaports have immediately implemented DT strategies. For example, Saigon Port, one of Vietnam’s busiest seaports, has developed the Saigon General Port Operations Management (SGPTOS) program, which includes Vietnam Terminal Operation System (VTOS) and General Cargo Terminal Operating System (GTOS), as part of its larger DT and to affirm its position. Saigon Port has implemented SGPTOS, which is synchronized and intended to satisfy all of the port, shipping line, and customer needs on a single system (Transportation, 2023). Despite the fact that the majority of seaports are engaged in DT projects, shipping firms appear to be unmotivated to convert their operation steps to digital. It is vital to investigate the factors influencing DT activities at shipping firms in order to integrate marine activities in general and the shipping industry in particular. The performance and drivers of DT for shipping companies will be investigated in the next section.
Drivers to the Digital Transformation in the Shipping Industry
Technological Factors
Digital Technology Development
The primary idea behind implementing DT in enterprises is to modify work and business processes based on digital-technology-driven advances; in this manner, company activities may be made more efficient (Figure 2). However, according to the TOE model, technological aspects include both internal and external technology at the organization’s disposal (Ngah et al., 2017). Furthermore, a technology’s inherent characteristics, such as complexity, usability, and learnability, have an important impact on its adoption (Ngah et al., 2017).
The research model.
The advancement of DT is dependent on technology variables such as artificial intelligence, the Internet of Things (IoT), and blockchain. According to several studies, artificial intelligence (Magistretti et al., 2019), IoT (Gopal et al., 2019; Salvini et al., 2022), blockchain technology (Hartley & Sawaya, 2019; Teng et al., 2022), and 5G networks have the important role in the development of DT (Attaran & Attaran, 2020). The usage of social media in management has an effect on the overall performance of businesses (Vardarlier & Ozsahin, 2021).
Despite being well known for a long time, artificial intelligence has truly taken off in the last few years. It is important to note the supportive roles played by other related technologies, such as cloud computing, IoT, and big data, in the growing popularity of artificial intelligence. Artificial intelligence has improved and grown more versatile because to these technologies. For instance, IoT enables real-time data interchange, big data offers endless resources for deep learning, and cloud computing offers an open platform for artificial intelligence. Additionally, the development of blockchain technology in 2018 can mitigate the risks that artificial intelligence poses to data security and data components while also offering trustworthy assurance for the application of artificial intelligence in numerous contexts (Teng et al., 2022). The value chain has changed as a result of digital technology, which is supplemented by other technologies including blockchain, big data, cloud computing, and artificial intelligence. There have been many new innovations, notably the digitalization of the shipping industry.
Digital Skills
High social complexity, structural rigidity, and procedural ambiguity characterize DT programs. As a result, today’s industry executives have a significant task in initiating, executing, and managing their company’s DT (Hoberg et al., 2015). Along with concentrating on daily operations, businesses are also utilizing DT to gain a competitive advantage. Building a talent pipeline is crucial to the DT process, since adopting a digital strategy need the assistance of digital talent who has business competencies, general knowledge, and digital thoughts as well as abilities (Teng et al., 2022). According to Kane (2019), individuals with business and DT abilities are the real key to effective DT.
Companies appear to have recognized the skill sets required to succeed in an increasingly digital environment (Sousa & Rocha, 2019). However, there is a substantial shortage of digital expertise in the relevant skill areas, which has delayed the adoption of DT (Hoberg et al., 2015). In a survey of companies dealing with DT challenges, just 17% agreed or strongly agreed with the statement “We have enough personnel with the skills necessary for our company’s digital transformation.” In contrast, 53% strongly disagreed with this claim (Hoberg et al., 2015).
Several studies have demonstrated the significance of digital skills and competencies. The rapid growth of individuals’ cognition and process capacities, for example, facilitates corporate digital transformation (Butschan et al., 2019). Bessonova et al. explored employee digital literacy as a factor in DT preparedness (Bessonova & Goryacheva, 2020). Employees’ digital mentality has been shown in studies to influence their involvement in or withdrawal from a company’s DT initiative (Solberg et al., 2020). SME growth and innovation success are connected to personal digital capabilities (Scuotto et al., 2021). However, several businesses have yet to begin DT, owing to a shortage of human capital and digital skills among staff (Bikse et al., 2021). To prepare for the future, firms must establish virtual human resource development and leverage learning resources (Teng et al., 2022). Based on the convincing proof presented above, we anticipate:
Organizational Factors
Financial Resource Availability
The organizational context relates to the firm’s internal traits and resources. It is characterized as formal and informal resources that promote technology adoption, including top-level management support, human skills and capabilities, and financial resources (Omrani et al., 2024). Financial resources are regarded as a factor that has a direct impact on the fulfillment of shipping companies’ DT programs in the preceding element. Financial resource availability, according to Nikopoulou et al. (2023), refers to money available for the adoption and application of digital technology (Iacovou et al., 1995).
According to studies, financial constraints will have a negative effect on technology development in general and DT in particular (Aranda-Usón et al., 2019; Su et al., 2013). For example, Aranda-Usón et al.'s (2019) analysis of policy and finance barriers to promoting clean technology in Chinese SMEs finds that external policy and financing barriers are more important than internal technical and managerial constraints (Su et al., 2013). As a result, the availability of funds, particularly for technological expenditures, is crucial for enterprises to participate in circular economy practices. According to Shahbazi et al. (2016), a significant management challenge is a lack of financial competence for environmental initiatives. Shipping businesses must acquire the necessary financial resources and prioritize their DT strategy due to resource restrictions. Firms that devote more financial resources to digital technology adoption are more likely to achieve success. As a result of the foregoing, the following hypothesis is proposed:
Top Management
The importance of top management in ensuring and driving the transition to Industry 4.0 has been recognized in the literature (W. Li et al., 2016; Porfírio et al., 2021). According to AlNuaimi et al. (2022), three practices of senior management can help organizations prosper in the digital age: (1) tracking emerging technology trends; (2) identifying the path of digital change and investment plan; and (3) directing the team to adapt quickly and precisely. Top management with DT attitudes, commonly referred to as “digital leaders,” may create collaborative networked companies and identify digital competencies (Bresciani et al., 2021; Frankowska & Rzeczycki, 2020). Transformational leadership has been notably addressed in research on leadership in a digital context. Transformational leaders inspire trust, strive to build leadership in others, sacrifice themselves, and act as moral agents, directing both themselves and their followers toward goals that go beyond the immediate requirements of the workgroup (Avolio, 1999).
Managers in the shipping industry have a critical role in developing strategies, encouraging people, and taking all risks associated with the DT process. For example, Raza et al. (2023) advise that executives in shipping businesses should encourage taking chances, conducting experiments, and supporting a fail-forward culture. According to Zhang et al. (2022), 58% of leaders interviewed claimed that top management is in control of their digital construction, indicating that top management is the most significant element. A company cannot successfully advocate for the DT supply chain without the direct backing of top management, who would explain the vision of DT and its connection with strategic objectives across departments (Daniels & Jokonya, 2020; Maduku et al., 2016). Zhang et al. (2022) showed that the more top managers support DT, the less resistance the company will encounter during the internal integration process, which is more favorable to increasing the company’s investment in DT, utilizing DT’s advantages, and being able to predict future development (Vogelsang et al., 2018). Additionally, management leadership is the key element in DT’s performance (Cichosz et al., 2020). More than ever, managers must have the skills to consistently analyze market trends, recognize and grasp technical breakthroughs, and translate them into commercial chances (Karimi & Walter, 2015). In addition, the leaders act as the change coordinator, encouraging stakeholders to get involved in the DT process and allocating resources effectively to ensure the smooth development of the DT. Therefore, we recommend that:
Environmental Factors
Government Regulations
Environmental context refers to the area in which a business conducts its operation, including government regulations (Nikopoulou et al., 2023; Tornatzky et al., 1990). Government regulations are incentives or limitations placed on company operations by the government (Awa et al., 2017). Numerous research have shown how strongly government rules affect the economy and society (Nikopoulou et al., 2023; Salwani et al., 2009; Zhu et al., 2004). As an illustration, when the COVID-19 pandemic first emerged, the government’s regulations on wearing masks and immunization to reduce mortality were successful (Nikopoulou et al., 2023). Government regulations are vital in the acceptance and deployment of digital technology, according to Salwani et al. (2009). As Baker (2011) highlighted, when governments place limits on industry, the use of digital technology can be pushed.
According to Henningsson and Eaton (2023), digital innovation regulation is critical to the production of efficient digital innovations. Researchers have found that government regulation of digital innovation may encourage the adoption of core infrastructure technologies through mandated use and glue together disparate islands of components by enforcing the adoption of interoperability standards by investigating phenomena such as the internet, smart electricity infrastructures, and international trade (Gheorghe et al., 2007; Henriksen & Damsgaard, 2007; Plantin et al., 2018; Rukanova et al., 2018; Sarker et al., 2021). One advantage of digital innovation regulation is that it resolves possible conflicts without depending on decision-makers to understand a whole system’s functioning or collectively agree on its aim (see Boyer, 1990).
Many regulations have been put in place in Vietnam to encourage DT efforts at seaports. For example, the Ministry of Transport’s Decision No. 2269/QD-BGTVT dated December 8, 2020, emphasizes the objective of promoting DT for fields, including the marine industry, by 2030 (Transportation, 2020). Resolution 24-NQ/TW dated October 7, 2022 “Digital transformation of logistics field, application of information technology to improve competitiveness.” As a result, seaports have rapidly developed and implemented DT into their operations. Based on the significance of regulations in DT strategies for the aforementioned activities, we think that:
Pressure From Competitors
Market competitiveness promotes corporate DT. In terms of the push to catch up, market competition leads enterprises to experience this pressure from competitors, and firms subsequently recognize new technology to preserve their competitive advantage (Hsu et al., 2014). Market competition is an outside factor that has an impact on a firm’s business environment, influencing its business model, sales strategy, scale expansion or contraction, and capacity to run and utilize its assets efficiently. Rising competition and greater market uncertainty require enterprises to engage in offensive or defensive competitive activities in order to preserve their competitive advantage (Yuan et al., 2021). When the market for new technologies becomes more competitive, organizations that can rapidly absorb and transform pressure exploit the notion of adopting new technology to fulfill their own strategic requirements. If enterprises are aware that their competitors are utilizing DT technologies, they will perceive a crisis and will swiftly lose their competitive advantage in the industry if new technologies are not implemented; hence, competitive pressures motivate DT (Alsaad et al., 2018; Châlons & Dufft, 2017; Jin & Pan, 2023; Mithas et al., 2013).
Several shipping firms in Vietnam, including Hyundai Merchant Marine, CMA CGM, Evergreen, and VOSCO, have made significant investments in DT, which increases efficiency and competitive advantage in the medium to long term (Nguyễn & Phan, 2023). These companies appear to be aware of increasing competitive pressure from competitors who use advanced technologies in their operations such as data and interface standardization, IoT container management, and blockchain technology to increase customer satisfaction (Jin & Pan, 2023; Nguyễn & Phan, 2023). To confront the challenge of dynamic environmental changes, small and medium-sized businesses incorporate digital technology with shipping services to obtain access to customer preferences and grow their market share. As a result, significant pressure from huge shipping lines has promoted small-scale enterprises’ DT efforts (Jin & Pan, 2023; Lyu et al., 2021).
The Performance of the Digital Transformation
Digital Transformation and Economic Performance
This research suggests that DT may accelerate economic performance, suggesting that each unit rise in DT leads to higher levels of economic performance. Today, DT has matured into a process of strategic change. As digital technology evolves, it gradually incorporates blockchain, AI, big data, and IoT to improve business operations, decision-making, organizational structure, and customer experience, resulting in the formation of a new business model (Ismail et al., 2017; Kuo et al., 2022; Sebastian et al., 2017). Through digital feedback and insight analyses, this approach will enable marketing and operations that previously depended entirely on intuition and industry knowledge to perform work efficiently and effectively, and will also lead to changes in business models (Henriette et al., 2016; Hess et al., 2016).
The use of AI technology, for instance, in the shipping sector will help with the completion of repetitive, time-consuming tasks, predictive maintenance of ships, and the loading and unloading of equipment, reducing labor costs, increasing efficiency, and boosting revenue (Kuo et al., 2022). Furthermore, the Internet of Things enhances distribution management in a variety of ways. RFID tags and GPS sensors enable business managers to follow the shipping process from start to finish. Real-time inquiries, quotes, space reservations, customs clearance, and inland delivery services are offered to customers through DT, which has reduced costs and increased productivity, improved logistics efficiency and competitiveness, improved business models, strengthened customer relationships, and produced sustainable logistics and business performance that exceeds the limitations of the past (Erceg & Sekuloska, 2019; Kuo et al., 2022; Lima & Pacheco, 2021; Nowicka, 2020; Schwertner, 2017). Therefore, we suggest that:
Digital Transformation and Environmental Performance
According to studies, DT enables businesses to gather operational data in real-time for remote monitoring devices, energy management, and predictive maintenance, hence lowering energy consumption and carbon emissions and achieving improved environmental performance (Chiarini, 2021; Y. Li et al., 2020).
Businesses must include environmental considerations in the creation of their products, services, and processes in order to achieve environmental performance (Schniederjans & Hales, 2016). In-product sensors may track the condition of a product’s components for reuse, recycling, and remanufacturing as well as capture all pertinent information regarding a product’s life cycle (Joshi & Gupta, 2019). Furthermore, the use of cloud computing, AI, and big data analytics may enhance information flow management, fostering the development of eco-process and eco-product innovations (Xu et al., 2023) as well as other green innovations (Dubey et al., 2019). To start, a digital strategy helps to innovate eco-processes. Given that information technology supports corporate processes, the digital strategy encourages the integration and coordination of functional areas including procurement, logistics, marketing, and operations (Bharadwaj et al., 2013). Monitoring and regulating energy use and emissions may be accomplished by enabling the deployment of data mining and analysis tools strategically to include high-quality information in the operational processes (Wei & Sun, 2021). Second, digital strategy enhances the development of eco-friendly products. Through effective and collaborative decision-making, cross-functional coordination, and the design of a digital strategy, products and services are able to satisfy expectations for going green (Fernando et al., 2021). In order to ensure that products are ecologically sustainable, eco-product innovation helps eliminate harmful components and fuels from them.
DT has a positive impact on eco-process and eco-product innovation in the shipping industry. Shipping companies may decrease time and labor and lower human costs by automating jobs, reducing unproductive stages, and streamlining service processes (Kuo et al., 2022). Additionally, DT helps reduce mistakes and improve dependability in the process by tracking and documenting information about items and locations as well as updating and exchanging data with pertinent parties. Delivery of services and DT assist in reducing waste and the environmental effect (Kuo et al., 2022; Pace, 2023). Considering what happened, we recommend that:
Method
Sampling and Data Collection
Currently, Vietnam has around 34,476 logistics service firms, comprising railways, roadways, maritime transport, inland waterways, aviation, and multimodal. In this study, the sampling frame consists of around 3,000 marine firms. The study was conducted on 560 firms using the convenience sample method. The advantages are that they are the most often used, less expensive, and do not require a list of all population characteristics (Acharya et al., 2013). However, this method has several disadvantages, which will be discussed in the limitations section. We gathered a list of survey participants by introducing the Vietnam Maritime Corporation and the Vietnam Logistics Association. Previously, they were informed of the survey’s objective and decided to participate voluntarily.
Participants in this study were managers of shipping businesses in Hai Phong, Quang Ninh, Nghe An, Da Nang, Khanh Hoa, Binh Dinh, Vung Tau, and Ho Chi Minh. All of these locations have seaports and a large number of shipping companies. The survey was distributed and data was collected between August 2021 and April 2023. This study collects data using a non-random technique using an online survey. The survey was translated into Vietnamese and then back into English to ensure that its contents were appropriately translated. The translation is done by highly educated individuals who teach the shipping in English. The authors collected email addresses and conducted a survey by introducing Vietnam Maritime Corporation. The authors emailed participants to explain the goal of the survey and to ensure the voluntariness and confidentiality of their personal information. There is a link to the online survey in the email.
The survey was conducted in two waves. The authors sent a survey questionnaire on the drivers and the implementation of DT in wave 1. A total of 560 questionnaires were distributed to participants at maritime companies. As a result, 482 value surveys were gathered, with an 86.1% response rate. There are 312 men and 170 women among them. In wave 2, 482 questionnaires evaluating the effectiveness of DT implementation were distributed to participants. There were 436 valid replies, representing 90.5% of the total. There are 294 men and 142 women among them. In terms of job titles, 37.9% of the respondents are presidents, while 25.7% are executives. Division managers and senior leaders make up 22.0% and 14.5% of the total. 44.5% of participants work for companies with 6 to 10 years in business, while 25.9% work for organizations with 0 to 5 years in business. Meanwhile, 17.4% and 12.2% of participants had been with the business for 11 to 15 years and above 15 years, respectively. Participants came from a variety of firms, including 43.4% from a company with fewer than 200 workers, 32.65 from a company with 201 to 400 employees, 14.2% from a company with 401 to 600 people, and 9.9% from a company with more than 600 employees (Table 1).
Characteristics of the Participants.
Measures
The longitudinal survey method was employed in the current study’s quantitative investigation. The questionnaire was organized into four sections: (1) demographics, (2) drivers of the DT implementation, (3) the implementation of DT, and (4) the performance of DT. In wave 1, participants were asked questions regarding demographics and the drivers of DT implementation. In wave 2, remaining questions about DT implementation and performance will be distributed. On a 5-point Likert scale, where 1 signified strongly disagree and 5 meant strongly agree, respondents indicated which statement most accurately described them. In Table 2, the operational definitions of each construct and reference are shown.
Construct Measures.
Digital Technology Development
We used six items to measure the digital technology development scale (Teng et al., 2022). The example statement is as follows: “Our company uses artificial intelligence in digital transformation activities”; “Our company uses blockchain technology in transporting goods.”
Digital Skills
The variable of the digital skills was measured by five items (Teng et al., 2022). The example statement is as follows: “Our company has digital skills employees”; “My company offers staff members opportunity or tools to develop the necessary digital skills for the digital transition.”
Financial Resource Availability
The scale of financial resource availability was measured by five items (Nikopoulou et al., 2023). The example statement is as follows: “Our company spends part of its financial resources on digital transformation activities”; “Our business has the funds necessary to buy digital equipment.”
Top Management
We used five items to measure this scale (Zhang et al., 2022). The example statement is as follows: “Top managers are responsible for building the digital strategy”; “Top managers are responsible for building a digital transformation plan.”
Government Regulations
The government regulation scale was measured by four items (Nikopoulou et al., 2023). The example statement is as follows: “The government issues regulations on digital transformation for transportation businesses”; “The government monitors digital transformation activities of transportation businesses.”
Pressure From Competitors
We applied four items to measure this scale (Jin & Pan, 2023). The example statement is as follows: “Competitors have implemented digital transformation”; “Competitors gain competitive advantage through digital transformation.”
Digital Transformation
To measure this variable, we employed five items based on the questions developed by L. Li (2022). The example statement is as follows: “Our company is aiming for a digital transformation program to better serve customers”; “In our company, we aim to digitize every step in the shipping process that can be digitized.”
Economic Performance
This variable was measured using 4 items developed by Teng et al. (2022). The example statement is as follows: “Increasing sales and return on sales may be achieved through digitally transforming your company”; “Your company may grow its gross profit by implementing digital transformation.”
Environmental Performance
We used 4 items in L. Li's (2022) study to measure this variable. The example statement is as follows: “Our business lowers waste emissions (solid, liquid, and air)”; “Our business reduces the use of hazardous or poisonous materials.”
Analyses
In order to do the statistical analysis for this study, we employed SPSS 22.0 and AMOS 22.0. We used a two-stage process for the data analysis (Anderson & Gerbing, 1988). Data analysis was used to first evaluate the convergent and discriminant validity of the proposed model’s multiple-item scale. These sorts of validity, or “the extent to which an operationalization measures the idea it is designed to assess” are what constitute concept validation, according to Bagozzi et al. (1990). To examine the measurement model, we conducted Principal Component Analysis (PCA) and Confirmatory Factor Analysis (CFA) using SPSS 22.0 and AMOS 22.0, respectively. Second, we used structural equation modeling (SEM) to assess structural models based on the cleaned measurement model.
Results
Principal Component Analysis (PCA)
Principal component analysis (PCA) with Promax rotation is the first analytical technique that is employed on the data. PCA is a dimensionality reduction technique that is frequently used to decrease the dimensionality of huge sets of data by condensing a vast collection of variables into a smaller one that preserves the majority of the data from the larger set. Smaller data sets are simpler to inspect and show, and machine learning algorithms evaluate data points far more quickly and easily when there are fewer irrelevant factors to consider (Hair et al., 2010).
Eight factors with eigenvalues larger than 1.0 have been found. All constructs combine to explain 63.6% of the variation. However, the screen plot shows a nine-component structure and the eigenvalue of the ninth factor is 0.94. The number of observed components was subsequently changed to 9, and the PCA was once more performed. The findings demonstrate that EcoPerform3 cross-loads on two constructs whereas EnviPerform3 cross-loads on a distinct DiTransform component. These two questions were dropped after careful consideration of their content and additional data analysis. All of the objects are placed in the designated buildings once these two items have been taken out. A total of 67.3% of the variation is explained by all constructs. Confirmatory factor analysis is then carried out.
Table 3 presents the factor analysis’s findings. All nine factors—digital technology development, digital skills, financial resource availability, top management, government regulations, pressure from competitors, digital transformation, economic performance, and environmental performance—were present, according to our results. With the exception of FiResource, which had a minimum loading factor of 0.599, all factors had loading factors that were more than 0.6.
Results of Factor Analysis.
Confirmatory Factor Analysis
A multivariate statistical method called confirmatory factor analysis (CFA) is used to assess the degree to which measured variables accurately reflect the number of components. Researchers can use CFA to identify the minimum number of components required in the data as well as the measurable variable that is connected to each latent variable. CFA is a method for approving or disapproving measurement hypotheses (Hair et al., 2010).
Confirmatory factor analysis (CFA) for the present study is carried out with the use of AMOS 22.0 software. The nine-factor model that was offered was a good fit for the data (χ2 = 936.881,
The reliability and consistency of the factors are evaluated using two metrics: Cronbach’s alpha (α) and composite reliability (CR). The phrase “internal consistency” refers to the process of evaluating the responses to a measure’s components’ within-scale consistency in order to assess a measure’s dependability. Only measuring tools with several items are affected. Two values have reportedly been used in place of one another, according to Hair et al. (2010) The CR and Cronbach’s alpha values should both be more than 0.7 (Hair et al., 2016).
In accordance with Table 4, every CR value for digital technology development (0.86), digital skills (0.77), financial resource availability (0.91), top management (0.87), government regulations (0.85), pressure from competitors (0.80), digital transformation (0.92), economic performance (0.83), and environmental performance (0.78) exceeded the cutoff value of 0.7. In addition, for each of the following factors—digital technology development, digital skills, financial resource availability, top management, governmental regulations, competitive pressure, digital transformation, economic performance, and environmental performance—all the items’ Cronbach’s alpha values—.86, .78, .90, .86, .84, .81, .92, .83, and .79—exceed the value of .7.
Results of Convergent Reliability Testing.
Using the “rule of thumb” outlined below, the average variance extracted (AVE) has frequently been employed to assess the discriminant validity. The positive square root of the AVE of any latent variable should be larger than its strongest association with any other latent variable. According to Fornell and Larcker (1981), the average variance extracted (AVE) value needs to be more than 0.5.
The findings indicate that, in that order, the AVE values for digital technology development, digital skills, financial resource availability, top management, government regulations, pressure from competitors, digital transformation, economic performance, and environmental performance are 0.55, 0.56, 0.57, 0.59, 0.51, 0.58, 0.60, and 0.57. These goods have standard factor loadings that are more than 0.50 and range from 0.599 to 0.92. According to Cheung and Wang (2017), all constructs have explained variances that are greater than 50%. As a result, it is conceivable to believe that all constructions are valid and convergent. Fornell and Larcker (1981) assert that the model’s variables have discriminant validity if the square root of AVE is greater than the variables’ inter-construct correlation coefficients. Additionally, the study’s model matched the data well. The study’s findings show that the suggested model has discriminant validity.
Common Method Variance
According to Tehseen et al.′s definition in their 2017 study, common method variance (CMV) is the systematic error variation that appears when variables are evaluated using the same source or approach (Le & Lin, 2023; Richardson et al., 2009). Therefore, the systematic error variance may lead to a bias. Due to respondents” consistent replies to all survey questions, the estimated correlation between variables may be overstated or understated (Richardson et al., 2009; Tehseen et al., 2017).
We used and looked at CMV prevention methods in this study. In order to stop respondents from guessing which qualities were associated with which factors (Le, 2023; Podsakoff et al., 2003), we initially employed a series of mixed questions. Additionally, we employed the most popular statistical techniques, such as Harman’s single-factor test and partial removal of the general concept, to assess the CMV in our study (Le, 2022; Tehseen et al., 2017). According to the calculated principal component analysis (PCA) results (Table 5), there were 9 distinct variables that together accounted for 67.3% of the total variance. The first unrotated component only accounted for 15.5% (less than 50%) of the data variation. No single factor emerges, and the first component does not explain the bulk of the variance. As a result, data analysis demonstrated that CMV was not present in this research.
Results of Total Variance Explained.
Hypotheses Testing
Structural equation modeling (SEM) was used to investigate the connections between dimensions and run path analysis. The direction of exogenous influences on endogenous variables was assessed using standardized coefficients, which were then used to test the hypothesis. The SEM test results show that the theoretical model’s goodness-of-fit indices are reasonably high (χ2 = 936.881,
The proposed model’s standardized path coefficient.
Table 6 displays the findings of our test of hypotheses. Our results show that in the context of technology, digital technology development and digital skills both positively affect the implementation of DT, with respective effects of (β = .22,
Hypothesis Testing Results.
The implementation of DT in shipping businesses is also positively influenced by organizational context characteristics such as financial resource availability and top management (β = .56,
Contrary to what we expected, just one factor of the environment context— governmental regulations has no impact on the adoption of DT (β = .12,
Our findings showed that the adoption of DT was positively correlated with the economic and environmental performances of the shipping enterprises, with a respective (β = .10,
Discussion
The goal of this research is to look into the factors that influence the implementation of DT in the maritime sector. Furthermore, this study seeks to confirm the impact of DT on the economic and environmental performance of shipping enterprises. Using the TOE framework to investigate technological, organizational, and environmental aspects impacting the adoption of DT, the research achieved the following significant results:
First, our study confirms that digital technology development (β = .22,
Second, our findings indicated that financial resource availability had the biggest influence on digital transformation in shipping companies (β = .56,
Third, this study finds that environmental factor (pressure from competitors) positively affects the DT implementation of shipping firms (β = .18,
Previous studies have demonstrated the role of government regulations in the DT process of businesses (Chong & Olesen, 2017; Nikopoulou et al., 2023). According to Chong and Olesen (2017), government regulations can act as either facilitators or obstacles in the digital technology process. Nikopoulou et al. (2023) reveal that government regulations significantly influence the DT adoption. However, contrary to our expectations, our results indicate that government regulations are unrelated to the implementation of DT in shipping firms (β = .12,
Finally, our findings demonstrate the influence of DT adoption on the performance of shipping enterprises. To begin, our findings find that the implementation of DT is positively associated with economic performance (β = .10,
Theoretical and Practical Implications
This study has some theoretical implications. First, we responded to Kuo et al.’s (2022) call for longitudinal approaches to evaluate the conceptual model, resulting in a greater understanding of how factors encourage the development of shipping DT. Second, our research has expanded the theoretical foundation of factors influencing DT implementation by thoroughly explaining the sub-factors of economic, organizational, and environmental factors (Jović et al., 2022). The TOE model is utilized in this study to illustrate how three distinct kinds of factors (technology, organization, and environment) influence the effectiveness of DT adoption. Besides, our findings can improve understanding of the internal and external influences on the implementation of DT, hence advancing research in related fields (Kuo et al., 2022). Third, the majority of current studies (Donepudi, 2014; Kuo et al., 2022; Raza et al., 2023; Tijan et al., 2021) lacks the universality of empirical evidence on the drivers and outcomes of DT adoption and change, instead relying on qualitative analysis to identify crucial variables. Our study also addressed the research shortcomings.
This research has a number of practical implications. Our findings provide managers of shipping businesses an economical and environmentally friendly solution. First, in the context of changing customer demands, fierce competition, and growing costs, shipping businesses may apply DT to reduce costs, enhance efficiency, and change business models in order to better serve customers. Integrating technology into the shipping industry can help these companies improve service delivery processes, transition from traditional paper bills to electronic invoices, and manage digital documents, which reduces paper usage, lowers administrative costs, and improves transaction transparency.
Second, International Maritime Organization (IMO) and other governments are currently focusing heavily on maritime environmental challenges, issuing numerous stringent regulations mandating shipping companies to minimize emissions from vessels and their activities. DT is actively helping to minimize fuel consumption by sharing data and utilizing smart transportation management systems to improve routes, cut travel time, and coordinate synchronous activities. Furthermore, DT improves vehicle management, reduces accidents and other risks, and contributes to greater environmental protection.
Third, this study has practical implications for managers in identifying the importance of factors influencing successful DT processes. Preparing a funding source will allow firms to be more proactive in DT activities such as investing in equipment, technology, and human resource training. The research findings also demonstrate the role of senior management in the DT process. Top managers are also aware of the importance of their contributions to the DT implementation process. They are in responsible for formulating DT objectives, obtaining finances for program execution, and guiding and inspiring staff members (Ali et al., 2022) to carry out DT effectively. Fitzgerald et al. (2014) argue that managers must aid employees in developing a digital identity and role inside the organization.
Finally, our findings have practical implications for policymakers. DT not only improves corporate productivity, but it also benefits the overall economy and the environment. As a result, the government should pay more attention to DT in the maritime sector. The government must set obligatory regulations and provides financial incentives to maritime enterprises to embrace DT. Furthermore, to assist the DT of the economy in general, and the transportation industry in particular, the government invests in research and development of transformational technologies such as blockchain, AI, and IoT.
Limitations and Future Research Orientations
Although this study has many implications, it does have several shortcomings. First, the data were obtained in Vietnam, and geographical restrictions may limit the findings’ generalization. Replicating this work in different regions of the world may increase the measurement models’ reliability and validity. Second, because the data were gathered by self-report, common method variance may be a problem in the current study. This study has the significant benefit of lowering concerns about common method variance for the primary outcome of interest by collecting data at various points in time (Podsakoff et al., 2003). As a result of the variables being gathered using the same technique or source, respondents may be more likely to give consistent replies to survey questions (Richardson et al., 2009; Tehseen et al., 2017). As a result, there may be an inflated or deflated correlation between the variables. Therefore, in order to reduce common method variance and establish the causality factors, future researchers should gather their data from other sources in different waves. Finally, one of the study’s disadvantages is that it used the convenience sampling method to choose the sample. This can lead to sampling bias, which lowers the generalizability and representativeness of research findings. To avoid sampling bias, we collected data from a variety of provinces and cities throughout North, Central, and South Vietnam. It is advised that future studies re-examine the research findings using different sampling methodologies.
Conclusion
This study used the TOE framework to investigate the factors that influence shipping companies’ DT processes and its performances. A survey of shipping business managers revealed that there are five major factors influencing the implementation of DT. The study also confirms the effect of DT on the economic and environmental performance of these companies. This research has significant theoretical and practical implications in the marine industry, particularly in light of the numerous fresh challenges that maritime transport faces.
Footnotes
Abbreviation Meaning
Declaration of Conflicting Interests
Funding
ORCID iD
