Factors Leading to the Digital Transformation Dead Zone in Shipping SMEs: A Dynamic Capability Theory Perspective

Abstract

Digital transformation (DT) has become a crucial driver of competitiveness in the shipping industry. However, many small- and medium-sized enterprises (SMEs) encounter barriers that result in digital transformation dead zones (DTDZs), where digital initiatives stagnate or fail to achieve the expected outcomes. This study investigates the key factors contributing to digital stagnation specifically within Vietnamese shipping SMEs, adopting the lens of the dynamic capabilities theory (DCT)—a framework that emphasizes firms’ abilities to sense opportunities, seize them, and reconfigure resources to maintain competitiveness in rapidly evolving environments. The DCT provides a dynamic and process-oriented perspective on how organizations adapt to technological change by building flexible and integrative capabilities. Based on quantitative data collected from 588 respondents across the Vietnamese shipping sector, the study employed structural equation modeling (SEM) to empirically assess the relationships among critical digital transformation variables. The findings reveal that inadequate sensing capabilities and a lack of data analytics are the most significant barriers, limiting firms’ ability to identify and act on digital opportunities. Additionally, limited ecosystem collaboration and supply chain fragmentation further exacerbate digital inertia. While poor reconfiguration capabilities and weak seizing capabilities also contribute to digital stagnation, their effects are comparatively weaker. The study offers theoretical contributions by extending the DCT, the resource-based view (RBV), and the ecosystem theory to the maritime sector, emphasizing the interplay between organizational, technological, and external barriers. Practical implications highlight the need for strategic investments in data analytics, ecosystem collaboration, and adaptive leadership to overcome digital stagnation.

1. Introduction

The shipping industry, a fundamental pillar of global trade, is undergoing a profound digital transformation (DT) to enhance operational efficiency, reduce costs, and meet growing demands for sustainability and transparency [1,2]. Emerging digital technologies—such as blockchain, artificial intelligence (AI), and the Internet of Things (IoT)—offer significant potential for optimizing logistics, improving supply chain visibility, and enhancing data-driven decision-making [3,4]. However, despite the recognized benefits of digitalization, many shipping companies encounter persistent challenges in implementing and sustaining DT initiatives. This phenomenon, referred to as the “digital transformation dead zone” (DTDZ), occurs when digital initiatives fail to progress, do not achieve anticipated outcomes, or remain misaligned with strategic objectives [5]. Given the increasing reliance on digital solutions in the global maritime industry, there is an urgent need to investigate the underlying causes of the DTDZ and explore strategies to mitigate its impact.

Existing research on digital transformation has primarily focused on broad industry contexts, often overlooking the unique structural, operational, and cultural challenges that hinder digital adoption in the shipping sector [5,6]. The shipping industry operates within a highly interconnected yet fragmented ecosystem, characterized by aging infrastructure, stringent regulatory requirements, and a deeply ingrained traditional business culture. These factors pose significant barriers to the adoption and integration of digital solutions [5,6]. Integrating insights from recent research, such as [7], who explored the strategic adoption of ICT during the COVID-19 crisis among Indian MSMEs, and Min [8], who examined pandemic-driven supply chain transformation, could further enrich the contextual backdrop of this study. These works emphasize how external shocks can accelerate or constrain digital responsiveness, offering parallel insights relevant to the maritime sector. While theoretical models of digital transformation have gained traction in the literature, limited research has been conducted on the specific factors contributing to the DTDZ in shipping. Additionally, the role of moderating variables—such as organizational readiness, leadership effectiveness, and external market conditions—remains largely unexplored [1,9]. Addressing these gaps is critical to advancing the understanding of digital transformation dynamics in the shipping industry and developing practical strategies to overcome them.

To bridge this research gap, this study systematically examines the key factors contributing to the DTDZ in shipping companies and investigates how these barriers can be effectively mitigated. Specifically, the study explores three critical dimensions of digital stagnation: organizational, technological, and external factors. Organizational factors, such as leadership misalignment, resistance to change, and insufficient digital skills, often create internal barriers that hinder transformation efforts [10,11]. Technological challenges, including the complexity of integrating emerging digital solutions with legacy systems, cybersecurity risks, and scalability concerns, further impede the DT process [6]. Meanwhile, external factors, such as regulatory pressures, market volatility, and competitive dynamics, add further layers of complexity to digital adoption and implementation [2,5]. Understanding how these factors interact and contribute to the DTDZ is essential for developing effective strategies to navigate digital transformation in the shipping industry.

This study adopts the dynamic capabilities theory (DCT) as a theoretical framework to examine how shipping companies can develop, deploy, and adapt their resources to overcome the challenges associated with digital stagnation [9,11]. The DCT emphasizes the ability of firms to sense emerging opportunities, seize them effectively, and continuously transform organizational structures to maintain competitiveness in an evolving environment. Given the rapid technological advancements and regulatory shifts affecting the shipping industry, the underdevelopment of dynamic capabilities may be a key driver of the DTDZ. By applying this theoretical perspective, this study explores how enhancing these capabilities can facilitate successful digital transformation and ensure long-term adaptability in the maritime sector.

The findings of this study are expected to contribute significantly to both academic literature and industry practice. From a theoretical perspective, this study addresses a critical gap by offering an in-depth, sector-specific analysis of the DTDZ in the shipping industry. By identifying key barriers, their interactions, and the role of moderating variables, the study extends existing discussions on digital transformation and broadens the application of the DCT in the maritime context. From a practical standpoint, the research provides actionable insights for industry stakeholders, offering guidance on how shipping companies can overcome digital stagnation through strategic leadership, digital partnerships, and regulatory adaptation [1,9].

By bridging these academic and practical gaps, this study contributes to a more comprehensive understanding of digital transformation challenges in the shipping industry while offering pragmatic solutions to help organizations avoid the DTDZ. Ultimately, it highlights the importance of developing dynamic capabilities as a means for shipping companies to successfully navigate digital transformation, harness emerging technologies, and maintain competitiveness in an increasingly digitalized global economy.

2. Literature Review

2.1. The Shipping in Vietnam

Vietnam’s import–export sector has been a critical driver of economic growth, with trade activities playing a significant role in the country’s integration into the global market. In recent years, Vietnam has experienced remarkable trade expansion, with total trade turnover reaching high values due to increasing export activities in manufacturing, electronics, and agriculture [12]. The country’s import–export structure is heavily influenced by key trade agreements such as the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) and the Regional Comprehensive Economic Partnership (RCEP), which have facilitated access to major global markets [13]. Additionally, fluctuations in exchange rates have been found to impact the competitiveness of Vietnamese export enterprises, influencing profit margins and trade balances [13]. The government’s trade strategy for 2021–2030 emphasizes sustainable growth by focusing on value-added exports, minimizing raw material exports, and enhancing the quality of imported goods [12]. As a result, Vietnam’s trade policies are increasingly geared towards maintaining trade surpluses while fostering innovation and industrialization.

Vietnam’s shipping industry is a vital component of its trade infrastructure, facilitating the transport of goods both domestically and internationally. The country has an extensive coastline and numerous ports, making maritime transport the backbone of its logistics network. The volume of goods transported via shipping is closely linked to GDP growth, with the expansion of trade leading to increased demand for shipping services [14]. Despite these advantages, Vietnam’s shipping industry faces significant challenges, such as reliance on foreign shipping lines, high logistics costs, and underdeveloped port facilities [15]. A substantial portion of Vietnam’s exports and imports are handled by international shipping firms, leading to a loss of potential revenue that could be retained within the domestic industry [15]. Additionally, investment in fleet expansion and modernization remains insufficient, creating bottlenecks in transport efficiency. To enhance competitiveness, there is a pressing need for capital investment in modern fleets, port infrastructure upgrades, and integration of smart logistics systems [16]. Strengthening the domestic maritime sector is crucial to reducing Vietnam’s dependency on foreign shipping firms and ensuring long-term economic sustainability.

2.2. Digital Transformation in Shipping

Digital transformation in the shipping industry refers to the comprehensive integration of digital technologies, such as the Internet of Things (IoT), artificial intelligence (AI), blockchain, and big data, to modernize traditional business processes and create value for stakeholders. It encompasses transitioning from analog systems to digital platforms, enabling real-time data sharing, predictive analytics, and enhanced decision-making capabilities. For instance, IoT technologies embedded in vessels allow for the continuous monitoring of equipment, leading to predictive maintenance and operational efficiencies [17]. Similarly, blockchain technology improves transparency and traceability in supply chains by securely recording and verifying transactions, thus reducing risks and increasing trust among stakeholders [18]. Advanced AI-powered tools, such as rotational ship detectors [19], while primarily used for maritime surveillance and navigation, exemplify the increasing role of intelligent vision systems in digital transformation efforts across the shipping industry. Moreover, digital transformation supports the automation of port and terminal operations, reducing reliance on manual processes and streamlining cargo handling to enhance efficiency and reduce costs [20].

While not within the scope of maritime transport, methodologies used in vehicular trajectory analysis (e.g., [21]) emphasize the growing importance of fine-resolution data in behavioral modeling. This underscores the potential benefits of integrating IoT-enabled monitoring and predictive analytics in shipping environments to track real-time container movement and optimize logistics decisions—key components of digital transformation maturity. These advancements not only improve operational workflows but also address environmental sustainability by optimizing fuel consumption and minimizing emissions [22]. Overall, digital transformation in the shipping industry is pivotal in enabling organizations to adapt to the dynamic global trade environment, fostering innovation, competitiveness, and resilience.

To ensure a more precise assessment of digital maturity within this context, we operationalize digital transformation across four key dimensions as informed by Vial [2] and Parida et al. [23]: (1) Digital Infrastructure Readiness, which evaluates the presence of core systems such as ERP, CRM, cloud computing, IoT connectivity, and cybersecurity protocols; (2) Process Digitalization, measured by the degree of digitization in logistics operations, such as automated cargo tracking and API-based integration; (3) Data Utilization Capabilities, reflecting the application of big data analytics, AI/ML tools, and real-time dashboards; and (4) Digital Service Innovation, captured through services like digital freight platforms and mobile booking systems. These dimensions collectively offer a robust and multidimensional framework to examine the depth and effectiveness of digital implementation in the shipping sector.

Digital transformation in the shipping industry has significant economic, environmental, and social impacts, reshaping the sector’s operations and its interactions with global supply chains. Economically, the integration of technologies such as blockchain, artificial intelligence (AI), and the Internet of Things (IoT) reduces operational inefficiencies; improves supply chain transparency; and decreases costs through automation and enhanced resource utilization [4,17,18]. For instance, predictive maintenance using IoT-enabled systems minimizes equipment downtime, leading to improved profitability [24]. Environmentally, digital technologies facilitate emission monitoring and route optimization, enabling shipping companies to reduce fuel consumption and meet global sustainability targets, such as the International Maritime Organization’s (IMO) carbon reduction goals [22,25]. Innovations like smart shipping systems, energy-efficient vessels, and autonomous ships further contribute to the industry’s transition towards greener operations [20,26]. Socially, digital transformation enhances the safety and well-being of maritime workers by automating hazardous tasks and introducing remote monitoring capabilities, which improve working conditions and reduce risks associated with manual interventions [17,27]. Moreover, the broader adoption of digital platforms fosters equitable access to global trade for smaller economies and marginalized regions, thereby promoting inclusivity in economic development [18]. By explicitly operationalizing these dimensions, the study contributes not only to the conceptual understanding of digital transformation but also enhances empirical measurement precision, enabling more accurate identification of digital transformation dead zones in shipping SMEs. Despite its transformative potential, challenges such as digital skill gaps, cybersecurity risks, and infrastructure limitations continue to hinder its full implementation. Thus, digital transformation in the shipping industry represents a critical driver of sustainable progress, balancing economic growth, environmental stewardship, and social equity.

2.3. Digital Transformation Dead Zone in Shipping

The concept of the “digital transformation dead zone” in shipping SMEs refers to the critical stagnation point where small- and medium-sized enterprises (SMEs) in the maritime industry struggle to implement digital technologies effectively, resulting in operational inefficiencies and competitive disadvantages. Despite the global push toward Industry 4.0, many shipping SMEs remain trapped in this dead zone due to financial constraints, inadequate digital infrastructure, and resistance to change [28]. The maritime industry is inherently reliant on complex logistical networks, and digitalization is crucial for optimizing processes such as freight management, real-time tracking, and automated documentation. However, SMEs often lack the resources to integrate digital supply chain solutions, which can lead to bottlenecks and an inability to meet evolving customer demands [29]. The dead zone phenomenon not only affects business performance but also contributes to a widening technological gap between large shipping corporations and SMEs, ultimately reducing the sector’s overall agility in responding to global supply chain disruptions [30]. Gupta and Kumar Singh [31] further contextualize this dead zone as a resilience breakdown, wherein MSMEs—paralleling SMEs in the shipping sector—are unable to respond adequately to environmental shocks due to liquidity constraints and low technical skills. Similarly, Varma and Dutta [32] underscore that post-pandemic stagnation often arises from a failure to realign digital strategies with changed customer expectations and the urgent need for operational transformation. These findings reinforce the view that the digital dead zone is not merely a technical hurdle but a systemic vulnerability in organizational adaptability and innovation. Overcoming this dead zone requires strategic investments in digital tools, government incentives, and industry-wide collaborations to facilitate smoother technological transitions for SMEs in the shipping sector.

The consequences of the digital transformation dead zone in shipping SMEs are profound, leading to operational inefficiencies, reduced competitiveness, and long-term sustainability risks. SMEs that fail to adopt digital technologies experience higher operational costs, slower logistics processes, and lower visibility in supply chain operations, limiting their ability to compete with larger, technologically advanced firms [28]. The lack of digital integration in areas such as automated freight tracking, cloud-based documentation, and AI-driven route optimization results in delayed shipments, mismanaged inventory, and increased error rates in logistical operations [29]. Furthermore, this digital gap exacerbates market exclusion, as SMEs struggle to integrate with global supply chains that increasingly demand real-time data-sharing and compliance with digital logistics standards [30]. The stagnation in digital transformation also impacts customer trust and service quality, as shipping SMEs become unable to offer the transparency and agility required in modern maritime logistics [33]. In the long run, remaining in the digital dead zone not only hinders financial growth but also increases the risk of obsolescence, as industry-wide technological advancements render traditional shipping models unsustainable.

2.4. The Dynamic Capabilities Theory

The dynamic capabilities theory (DCT) posits that firms achieve and sustain competitive advantage not merely through ownership of valuable resources but through their ability to develop, adapt, and reconfigure internal and external competencies in response to rapidly changing environments [34]. In contrast to the resource-based view (RBV), which emphasizes the role of static assets, the DCT focuses on dynamic, process-oriented abilities that enable firms to sense emerging opportunities, seize them strategically, and transform organizational resources to meet evolving market demands [11,35]. Key dynamic processes—such as learning, innovation, strategic foresight, and adaptive decision-making—allow organizations to continuously evolve their value propositions in volatile environments characterized by technological turbulence, shifting regulations, and global competition. Empirical research confirms the criticality of these capabilities in fostering organizational resilience and agility [36].

Recent scholarship has further expanded the DCT’s applicability in light of the COVID-19 pandemic, where firms’ survival often depended on how swiftly they could adapt to disrupted supply chains and digitized operational models. For example, Sreenivasan et al. [37] applied a Total Interpretive Structural Modeling (TISM) approach to identify foundational resilience factors—such as leadership responsiveness and digital infrastructure—that act as dynamic enablers for start-ups under crisis conditions. Their findings align with the DCT’s core premise that adaptability and reconfiguration are vital for navigating external shocks. Similarly, El Khoury et al. [38] extended the DCT into the domain of green supply chain practices, demonstrating that environmental responsiveness and sustainability integration are not peripheral choices but dynamic competencies that drive long-term competitiveness. Their work illustrates how firms must transform not only their technologies but also their strategic frameworks in response to ecological imperatives.

Furthermore, Qrunfleh et al. [39] synthesized pandemic-era supply chain research to highlight how organizational capabilities evolved in the face of severe logistical disruptions. Their meta-analysis emphasizes that firms with robust dynamic capabilities—particularly in sensing demand volatility and swiftly realigning operations—were better positioned to absorb shocks and sustain market presence. Together, these studies provide a compelling expansion of the DCT, illustrating its relevance across varied domains—from crisis resilience in start-ups to sustainable supply chain transformation and pandemic-responsive agility. This reinforces the theory’s central proposition: dynamic capabilities are not just reactive tools but foundational drivers of strategic renewal and long-term viability in uncertain environments.

2.5. Hypothesis Development

Theoretical and research hypotheses are illustrated in Figure 1, which outlines the conceptual model developed for this study.

2.5.1. Inadequate Sensing Capabilities

Inadequate sensing capabilities, defined as the limited ability of firms to detect and interpret digital opportunities and technological trends, constitute a fundamental barrier to digital transformation among small- and medium-sized shipping enterprises (SMEs). Unlike industry leaders such as Maersk and Hapag-Lloyd—who have successfully implemented advanced digital strategies, including predictive analytics, API-integrated logistics, and blockchain-powered trade platforms—SMEs frequently misread both market signals and their own organizational readiness, leading to failed initiatives and resource waste [40,41]. These global carriers maintain dedicated digital sensing infrastructures that enable them to monitor market shifts and quickly capitalize on technological innovations, creating strategic advantages in efficiency, transparency, and customer service [42]. In contrast, many SMEs invest considerable time and capital in digital projects without fully understanding the external competitive landscape or internal digital capabilities. This misalignment typically results in partial implementations, unused systems, or technology abandonment—hallmarks of the digital transformation dead zone (DTDZ) [43]. Two core deficiencies underlie this sensing failure: First, the absence of market sensing restricts SMEs’ awareness of emerging logistics innovations like AI-enhanced route optimization or IoT-based container monitoring, delaying their digital response and diminishing competitive relevance [44]. Second, poor organizational sensing leads SMEs to overestimate their digital maturity, initiating projects beyond their absorptive and technical capacities, which often culminates in operational breakdowns and change fatigue [45,46]. These inadequacies, when unaddressed, entrench SMEs in a state of transformation inertia, limiting their ability to adapt and survive in a digitally evolving maritime industry.

Hypothesis 1.

Inadequate sensing capabilities is directly related to the digital transformation dead zone in shipping SMEs.

2.5.2. Weak Seizing Capabilities

Seizing capability, within the framework of the dynamic capabilities theory (DCT), refers to an organization’s ability to mobilize internal and external resources to capture value from sensed opportunities, such as investing in digital technologies, reallocating assets, or transforming business models [11]. In the shipping sector, this capability is crucial for leveraging innovations such as API-based tracking systems and IoT-enabled fleet management. However, small- and medium-sized enterprises (SMEs) typically exhibit weaker seizing capabilities than large corporations due to constraints in financial, technological, and managerial resources [47,48]. This disparity is particularly acute in digital transformation (DT), where SMEs often fail to move beyond piecemeal adoption of isolated technologies, resulting in fragmented, non-integrated systems [44,49]. For instance, although SMEs in the shipping industry recognize shifts in customer expectations—such as real-time container tracking—they often hesitate to adopt advanced systems due to uncertainty, risk aversion, and a lack of sustained investment motivation [50,51]. The absence of a comprehensive digital strategy and internal champions further contributes to what scholars label the “Digital Transformation Dead Zones” (DTDZs), zones where transformation initiatives stagnate or regress [52]. In these contexts, seizing capabilities are not only underdeveloped but are also impeded by systemic issues such as outdated infrastructure and weak cross-functional collaboration. Ultimately, SMEs in the shipping industry are trapped in a cycle of reactive rather than proactive transformation, leaving them vulnerable to competitive displacement in increasingly digitized global logistics networks. Thus, we argue the following:

Hypothesis 2.

Weak seizing capabilities are directly related to the digital transformation dead zone in shipping SMEs.

2.5.3. Poor Reconfiguration Capabilities

Poor reconfiguration capabilities in small- and medium-sized enterprises (SMEs) in the shipping industry present significant challenges to their competitiveness and sustainability. These firms often lack the financial and technological resources necessary to effectively reconfigure their operations in response to market fluctuations, regulatory changes, and technological advancements [53]. Due to their constrained access to capital and limited strategic planning, shipping SMEs struggle with integrating dynamic capabilities that enable adaptation and renewal of their business models [54]. Studies have highlighted that the digital transformation in maritime logistics, which requires high investment in data analytics and automation, is a particular pain point for SMEs, as their low levels of digital maturity hinder their ability to leverage new technologies for operational reconfiguration [55]. Furthermore, SMEs in the shipping sector often rely on outdated legacy systems, making it difficult to swiftly adopt modern supply chain solutions, leading to inefficiencies and reduced resilience during global disruptions such as the COVID-19 pandemic [56].

Recent studies underscore the importance of digital transformation in maritime trade, particularly for SMEs that must develop reconfiguration capabilities to sustain competitive advantages [57]. SMEs face unique challenges due to a lack of financial resources and expertise, making it difficult for them to integrate advanced technologies such as blockchain-based trade documentation, AI-powered logistics, and digital fleet management [58]. Research by Fellenstein and Umaganthan [59] shows that digital transformation requires SMEs to dynamically reconfigure business models, a key challenge in the logistics and transportation sector. Moreover, limited investment in strategic IT alignment has been identified as a major barrier preventing SMEs from effectively leveraging digital technologies for sustainable competitive advantages [54]. Consequently, their inability to reconfigure and redeploy resources dynamically often results in weaker performance, higher operational costs, and vulnerability to competitive pressures from larger shipping companies with more agile capabilities [60].

Poor reconfiguration capabilities in shipping SMEs often lead to what is termed the digital transformation dead zone (DTDZ), a stagnation point where firms fail to progress toward comprehensive digitalization. Reconfiguration capabilities—defined as the ability to restructure operational processes, integrate new technologies, and align business strategies dynamically—are critical for effective digital transformation. However, many shipping SMEs lack the financial resources, expertise, and digital maturity needed to implement flexible technological upgrades, resulting in outdated legacy systems and fragmented operational structures [55]. A study by Gao et al. [61] highlights that SMEs need to develop reconfiguration capabilities as part of their servitization strategies in the digital era, particularly in manufacturing and shipping. Similarly, Rupeika-Apoga et al. [62] find that firms that integrate digital orientation and reconfiguration capabilities are more likely to navigate the challenges of digital transformation successfully. Without adequate reconfiguration capabilities, SMEs cannot effectively integrate digital tools, leaving them in a state of digital inertia that hinders competitiveness and resilience [63].

Hypothesis 3.

Poor reconfiguration capabilities are directly related to the digital transformation dead zone in shipping SMEs.

2.5.4. Supply Chain Fragmentation

Supply chain fragmentation in small- and medium-sized enterprises (SMEs) within the shipping industry poses significant operational and strategic challenges, often leading to inefficiencies and higher costs. Philipp et al. [64] highlight that maritime SMEs struggle with fragmented databases and a lack of digital integration, resulting in supply chain slowdowns and suboptimal decision-making. Similarly, Hamisi [28] notes that Tanzanian SMEs face difficulties adapting to global supply chain management practices due to infrastructure limitations and weak interconnectivity among stakeholders. Blockchain technology and smart contracts have been proposed as potential solutions to enhance transparency and reduce fragmentation in maritime logistics [64]. Kot, Haque, and Baloch [65] argue that supply chain fragmentation in SMEs is often a consequence of region-specific operational constraints, leading to inconsistent delivery schedules and inefficiencies in long-term supplier relationships.

Recent research highlights that supply chain fragmentation in shipping SMEs exacerbates inefficiencies and reduces the potential benefits of digital transformation, making digital adoption more complex [66]. Ziyadullaev [67] argues that while digital platforms enhance supply chain visibility, high fragmentation and a lack of standardization continue to pose challenges for SMEs in the freight industry. In the maritime logistics industry, Yuen and Thai [68] identify barriers such as misaligned incentives, insufficient technological adoption, and siloed communication as key contributors to integration failure. McCamley and Gilmore [69] further emphasize that supply chain fragmentation exacerbates SME dissatisfaction due to delays and inefficiencies in the delivery process. Addressing these challenges requires a holistic approach, integrating digital solutions, fostering collaborative networks, and optimizing logistics strategies to mitigate fragmentation and enhance the efficiency of shipping SMEs [65,68].

Supply chain fragmentation in shipping SMEs contributes to what scholars term a “digital transformation dead zone,” where businesses struggle to implement and scale digital innovations effectively. According to Ozkan [70], the highly fragmented nature of logistics and supply chain networks in SMEs leads to a lack of data synchronization, outdated legacy systems, and a general resistance to digital change. This misalignment hampers digital integration efforts, leaving many shipping SMEs unable to fully capitalize on new technologies such as blockchain, AI, and IoT. Omowole and Olufemi-Philips [71] identify that the biggest barriers to digital transformation in SMEs stem from fragmented supply chains and the lack of strategic digital alignment, which prevents the seamless integration of innovative technologies. Hamisi [28] further asserts that the digital divide is exacerbated by weak interconnectivity and inconsistent data-sharing practices, limiting SMEs’ ability to engage in real-time logistics optimization. McAuliffe [72] emphasizes that supply chain fragmentation not only increases inefficiencies but also reduces SMEs’ agility in responding to market changes, thereby pushing them into a digital stagnation zone. Additionally, Büyüközkan and Göçer [73] highlight that fragmented supply chains often create silos, making it difficult for SMEs to develop integrated digital solutions that foster end-to-end visibility and operational efficiency. Nguyen [74] provides empirical evidence that Vietnamese shipping SMEs struggle with fragmented logistics networks, which limits their ability to scale digital adoption and engage in real-time decision-making. Based on the above evidence, we believe the following:

Hypothesis 4.

Supply chain fragmentation is directly related to the digital transformation dead zone in shipping SMEs.

2.5.5. Limited Ecosystem Collaboration

The limited ecosystem collaboration in shipping small- and medium-sized enterprises (SMEs) presents significant challenges to operational efficiency, innovation, and sustainability in the maritime sector. Recent research by Suboyin et al. [75] underscores that ecosystem collaboration in shipping is becoming critical for achieving sustainability goals and fostering digital transformation, particularly through shared emission-reduction strategies and global logistics partnerships. The adoption of blockchain and smart contracts has been proposed as a mechanism to improve trust and reduce transaction costs among SMEs in maritime supply chains; however, implementation remains limited due to technological and financial barriers [64]. Additionally, ecosystem collaboration within SME clusters is often hindered by the lack of a coherent digital platform that facilitates seamless coordination, as evidenced by Liu et al. [76]. According to Piot-Lepetit [77], SMEs across various supply chains face challenges in building trust-based digital ecosystems, which leads to inefficiencies and weak participation in larger networks. Addressing these challenges requires targeted policy interventions, improved technological accessibility, and enhanced knowledge-sharing frameworks to enable shipping SMEs to fully participate in the maritime industry’s digital transformation.

The phenomenon of the “digital transformation dead zone” in shipping SMEs is largely attributed to limited ecosystem collaboration, which impedes technological advancements and integration into digital supply chains. Stroumpoulis et al. [78] highlight that sustainable supply chain management necessitates robust digital collaborations, yet many shipping SMEs are unable to engage due to the absence of interoperable digital platforms and standardized frameworks. This results in operational inefficiencies and a lack of access to advanced maritime logistics solutions. Similarly, Matenga and Mpofu [79] argue that the reluctance of SMEs to participate in blockchain-based digital ecosystems exacerbates their technological stagnation, as these firms remain locked into traditional supply chain practices. Nazo [80] identifies that self-service digital platforms are emerging as a way to automate infrastructure provisioning, offering SMEs a more accessible entry point into digital collaboration. Furthermore, Rapaccini, Saccani, and Kowalkowski [81] emphasize that digital transformation requires a shift in business models, but without ecosystem collaboration, SMEs lack the necessary external knowledge and resources to implement disruptive technologies effectively.

The “dead zone” effect is further reinforced by financial constraints and inadequate government support, preventing SMEs from investing in the digital infrastructure needed to remain competitive [82]. Research by Piot-Lepetit [77] highlights that even in highly digitized industries, SMEs struggle with fragmented value chains, making digitalization difficult to scale effectively without ecosystem support. As a result, shipping SMEs that fail to integrate into digital ecosystems risk obsolescence in an industry that increasingly relies on real-time data sharing, automation, and smart logistics. Therefore, we argue the following:

Hypothesis 5.

Limited ecosystem collaboration is directly related to the digital transformation dead zone in shipping SMEs.

2.5.6. Lack of Data Analytics

The lack of data analytics capabilities is a critical barrier to digital transformation in small- and medium-sized enterprises (SMEs) operating within the shipping industry. Research indicates that insufficient data mining and analytical tools hinder effective decision-making, preventing SMEs from leveraging digital transformation for operational efficiency and competitiveness [66]. Many shipping SMEs lack business intelligence (BI) and machine learning (ML) systems, which are essential for processing large volumes of data and optimizing logistics operations [25]. The absence of such tools limits their ability to identify market trends, predict operational risks, and optimize decision-making, thereby restricting their digital adoption strategies.

A significant impact of insufficient data analytics is the weakening of sensing capabilities, which refers to a firm’s ability to detect technological trends and market changes [11]. Without access to real-time data processing tools, SMEs struggle to identify emerging digital opportunities such as blockchain-enabled shipping documentation, AI-driven fleet management, and predictive maintenance solutions [66]. As a result, these firms often fail to anticipate and respond to technological advancements, leading to delayed or misaligned digital transformation efforts. In contrast, larger shipping corporations with advanced analytics systems can efficiently track market fluctuations, fuel price trends, and regulatory changes, enabling them to adapt and innovate proactively [25]. Moreover, data analytics deficiencies directly affect seizing capabilities, which involve assessing when and how to deploy digital technologies effectively [83]. SMEs without robust analytics tools lack data-driven insights to evaluate digital transformation investments, resulting in hesitation in adopting new technologies such as cloud-based logistics platforms and automated port management systems [84]. This inability to measure the potential return on investment (ROI) of digital adoption leads to prolonged reliance on outdated manual systems, reducing efficiency and increasing operational costs [85]. Consequently, SMEs are unable to transition towards data-driven decision-making, further trapping them in a digital transformation dead zone (DTDZ).

In addition, weak data analytics capabilities hinder reconfiguring capabilities, which are essential for adapting business models in response to market shifts [11]. The shipping industry is increasingly reliant on digital ecosystems, where data-driven optimization of supply chain operations is necessary for maintaining competitiveness [74]. SMEs that lack data integration tools struggle to adjust pricing strategies, optimize shipping routes, and streamline operations, resulting in operational inefficiencies and declining profitability [86]. In contrast, firms equipped with real-time analytics solutions can swiftly adapt to shifting trade regulations, geopolitical disruptions, and environmental compliance requirements, ensuring long-term sustainability in a volatile industry [87]. Given these challenges, this study hypothesizes that the lack of data analytics is directly related to the digital transformation dead zone in shipping SMEs.

Hypothesis 6.

A lack of data analytics is directly related to the digital transformation dead zone in shipping SMEs.

3. Methodology

3.1. Sampling and Data Collection

Data collection for this study was conducted through an online survey, facilitated by introductions to transportation companies in Vietnam via the Vietnam Maritime Administration. A convenience sampling method was employed, ensuring accessibility and ease of participation for industry professionals. Prior to data collection, participants were provided with a detailed introduction outlining the purpose of the survey, emphasizing its academic and practical significance in understanding the digital transformation challenges faced by shipping SMEs. Informed consent was obtained from all respondents, ensuring voluntary participation and adherence to ethical research guidelines. The survey targeted managerial and operational personnel within shipping firms, as they possess critical insights into the strategic and technological barriers affecting digital transformation. This approach ensured the collection of relevant and high-quality data, reflecting the current state of digital adoption and stagnation within Vietnam’s maritime industry. While convenience sampling inherently limits statistical generalizability, it was considered suitable for this exploratory study given the challenges in accessing a fully randomized sample within Vietnam’s fragmented and geographically dispersed shipping SME sector. To improve representativeness, a stratified convenience sampling strategy was employed, ensuring the inclusion of participants across varying firm sizes (micro, small, and medium) and geographical regions (North, Central, and South Vietnam). This approach allowed for a more balanced and contextually rich dataset, reflecting the diversity of organizational structures and digital maturity levels across the maritime landscape.

Table 1. Characteristics of the participants.

	Variable	Category	Frequency	Percentage (%)
1	Gender	Male	386	65.65
		Female	202	34.35
2	Qualifications	Secondary/College	121	20.58
		University	264	44.90
		Master’s	186	31.63
		PhD and above	17	2.89
3	Age	Under 30 years old	142	24.15
		30–39 years old	265	45.07
		40–49 years old	124	21.09
		50 years old and above	57	9.69
4	Position	Senior management & leadership	92	15.65
		Head of information technology	65	11.05
		Logistics management	142	24.15
		Technical and operation staff	241	40.99
		Others	48	8.16
5	Types of companies	Under 50 people	56	9.52
		51–100 people	192	32.65
		101–150 people	123	20.92
		151–200 people	139	23.64
		Over 200 people	78	13.27

presents demographic and organizational characteristics of employees in small- and medium-sized enterprises (SMEs) within the maritime transport sector. In terms of gender distribution, the workforce is predominantly male (65.65%), suggesting a gender imbalance that aligns with the traditionally male-dominated nature of the shipping industry. Educational qualifications indicate a well-educated workforce, with 79.42% holding a university degree or higher, including 31.63% with a master’s degree and 2.89% with a PhD, while only 20.58% have secondary or college-level education. Age distribution reveals that the largest group falls within the 30–39 age range (45.07%), highlighting the predominance of mid-career professionals, whereas younger employees under 30 account for 24.15%, and those aged 50 and above comprise only 9.69%, suggesting a relatively younger workforce. Regarding job positions, the majority (40.99%) work in technical and operational roles, followed by logistics management (24.15%), senior management (15.65%), and IT leadership (11.05%), indicating that most employees are involved in direct operational activities rather than strategic decision-making. The distribution of company sizes shows that the most common SME size is 51–100 employees (32.65%), while companies with 101–150 (20.92%) and 151–200 employees (23.64%) are also prevalent, whereas smaller companies with fewer than 50 employees are less common (9.52%). These findings suggest that medium-sized firms dominate the sector, and while the workforce is highly educated and relatively young, there are still challenges in gender diversity and leadership distribution.

3.2. Scales and Measures

To measure the key constructs in this study, participants’ responses were collected using a five-point Likert scale, where 1 represented “strongly disagree” and 5 represented “strongly agree.” To ensure the validity and reliability of the measurement model, an exploratory factor analysis (EFA) was conducted using the maximum likelihood extraction method with Promax rotation. A threshold value of 0.50 was applied to assess the significance of the identified components, ensuring that the scale items effectively captured the underlying constructs. This rigorous approach enhanced the robustness of the measurement model and facilitated a comprehensive understanding of the factor structure.

3.2.1. Inadequate Sensing Capabilities

To evaluate the construct of inadequate sensing capabilities, the author developed a scale consisting of five items, ensuring its reliability through references to established theoretical frameworks and prior empirical studies [77,88,89]. This scale captures the extent to which deficiencies in real-time data collection and market sensing hinder digital transformation within shipping companies. For instance, one of the items highlights how the lack of real-time data collection and processing results in poor decision-making and delays in adopting digital innovations. Another item underscores the inability to sense market trends and sustainability demands, which restricts the organization’s ability to integrate new technologies (Appendix A). The internal consistency of the scale was confirmed with a Cronbach’s alpha coefficient of 0.840, indicating a high level of reliability.

3.2.2. Weak Seizing Capabilities

A five-item scale was developed to assess the variable weak seizing capabilities, reflecting a company’s ability to respond to and integrate digital innovations. This scale was formulated based on existing theoretical models and previous empirical findings [9,90,91,92]. It examined structural inefficiencies that hinder a shipping company’s ability to seize digital opportunities. One of the items addressed the challenges organizations face in adapting and integrating digital technologies due to inefficient decision-making structures. Another item highlighted the lack of investment in workforce training, which restricts employees from effectively utilizing digital tools and platforms. The scale exhibited high reliability, with a Cronbach’s alpha of 0.883.

3.2.3. Poor Reconfiguration Capabilities

The poor reconfiguration capabilities was measured using a six-item scale, grounded in theoretical insights and prior research [27,66,93,94,95,96]. This scale assessed the extent to which shipping companies struggle to modify or optimize their digital infrastructure in response to evolving operational demands. For example, one item highlighted how ineffective reconfiguration of digital processes leads to inefficiencies in supply chain management and logistics operations. Another item emphasized the rigid operational structures that hinder digital transformation and prevent companies from responding dynamically to technological advancements. The scale demonstrated a strong reliability score, with a Cronbach’s alpha of 0.878.

3.2.4. Supply Chain Fragmentation

To examine supply chain fragmentation, a five-item scale was constructed based on theoretical foundations and empirical literature [43,97,98,99,100]. This scale measured inefficiencies arising from disconnected or poorly integrated supply chain processes within shipping companies. One item highlighted how fragmented supply chain structures create difficulties in tracking and managing shipments effectively. Another item pointed to inconsistent communication and data-sharing practices among supply chain stakeholders, which result in operational inefficiencies. The internal consistency of this scale was confirmed with a Cronbach’s alpha of 0.850.

3.2.5. Limited Ecosystem Collaboration

A four-item scale was developed to assess limited ecosystem collaboration, with items designed based on theoretical and empirical literature [77,101,102]. This scale captured the extent to which shipping companies engage in collaborative efforts with external stakeholders, including digital service providers and other maritime industry players. One of the items highlighted the restricted collaboration between shipping companies and digital service providers, which inhibits the adoption of integrated digital platforms. Another item addressed the lack of trust and transparency in data-sharing practices, which impedes digital transformation initiatives. The scale demonstrated high reliability, with a Cronbach’s alpha of 0.884.

3.2.6. Lack of Data Analytics

The lack of data analytics construct was assessed using a four-item scale, developed based on theoretical insights and prior research [66,103]. This scale captured the extent to which inadequate data analytics capabilities hinder strategic decision-making and operational efficiency within shipping companies. One of the items emphasized how the absence of data analytics tools limits the company’s ability to predict market trends and operational risks. Another item highlighted how inefficiencies in data utilization contribute to poor resource allocation and logistics management. The internal consistency of this scale was robust, with a Cronbach’s alpha of 0.891.

3.2.7. Digital Transformation Dead Zone

The digital transformation dead zone was measured through a seven-item scale, developed based on theoretical foundations and prior studies [2,4,23,26]. This scale evaluated the extent to which digital transformation initiatives face systemic obstacles that lead to stagnation or failure. One of the items highlighted how organizational resistance to change frequently causes digital transformation efforts to stall. Another item addressed the misalignment between digital transformation strategies and core business objectives, which impedes meaningful progress. The scale demonstrated exceptionally high reliability, with a Cronbach’s alpha of 0.936.

3.3. Analyses

This study employed a quantitative research methodology to systematically collect and analyze data, utilizing SPSS 22.0 and AMOS 22.0 as analytical tools. The primary objective of the analysis was to comprehensively examine the study’s findings through a rigorous two-stage analytical approach, as proposed by Anderson and Gerbing [104]. The first phase of data analysis focused on assessing the convergent and discriminant validity of the multi-item scale used in the proposed model. According to Bagozzi et al. [105], convergent validity refers to the extent to which different measures of the same construct are correlated, while discriminant validity assesses whether constructs that are theoretically distinct do not exhibit high correlations. Establishing both types of validity is crucial to ensuring that the operationalized variables accurately measure the intended theoretical constructs, thereby enhancing the study’s overall validity and reliability. This methodological rigor provides a robust foundation for interpreting the research findings.

To evaluate the measurement model, the study employed a combination of Principal Component Analysis (PCA) and Confirmatory Factor Analysis (CFA). The analyses were conducted using SPSS 22.0 and AMOS 22.0, allowing for an in-depth examination of the reliability and validity of the measurement constructs. This dual analytical approach ensured that the model’s measurement properties were thoroughly assessed, establishing a solid empirical basis for subsequent evaluations. Following the validation of the measurement model, structural equation modeling (SEM) was utilized to assess the structural model, which was developed based on the refined measurement framework. By applying SEM, the study was able to investigate the relationships between the latent constructs, test the hypothesized paths, and derive meaningful insights into the underlying structural dynamics. This comprehensive methodological approach facilitated a deeper understanding of the interrelationships among key variables, ultimately contributing to a more nuanced interpretation of the study’s findings.

4. Results

4.1. Principal Components Analysis

The data was analyzed using Principal Component Analysis (PCA) with Promax rotation, leading to the identification of six components with eigenvalues greater than 1.0. These components collectively accounted for 70.61% of the total variance, indicating that the extracted factors effectively captured the underlying patterns within the dataset. This exploratory analysis provided fundamental insights into the latent structure and interrelationships among variables, serving as a solid foundation for subsequent validation and interpretation.

The scree plot suggested the presence of a seven-factor structure, with each component displaying an eigenvalue above the 1.0 threshold. Given this observation, we reassessed the Principal Component Analysis (PCA) and expanded the number of extracted components to seven, ensuring a more precise and comprehensive factor representation. Upon conducting this revised analysis, we found that items “Collaboration4” and “Analytic5” exhibited cross-loadings on multiple constructs, indicating potential measurement overlap. A thorough review of the wording and conceptual alignment of these items revealed that their removal would enhance the clarity and reliability of the measurement model.

Following this refinement, the revised model accounted for 68.61% of the total variance while maintaining a clearer and more distinct factor structure. This modification ensured that all remaining items were properly aligned with their respective constructs, thereby strengthening the validity and consistency of the measurement instrument. To further confirm the robustness of the revised factor structure, a Confirmatory Factor Analysis (CFA) was conducted. This step aimed to assess the model’s reliability, convergent validity, and discriminant validity, thereby ensuring the stability and theoretical soundness of the measurement framework.

4.2. Confirmatory Factor Analysis

To assess the construct validity of the refined measurement model, Confirmatory Factor Analysis (CFA) was employed using AMOS 22.0, following best practices in quantitative research methodology. The objective was to evaluate the model’s overall goodness of fit and confirm the adequacy of the proposed factor structure in capturing the latent constructs. The analysis yielded robust fit indices, indicating satisfactory model fit: χ² (568) = 1262.926, χ²/df = 2.223, CFI = 0.948, TLI = 0.942, GFI = 0.892, IFI = 0.948, and RMSEA = 0.046. These results align with the fit criteria recommended by Hu and Bentler [106], wherein acceptable models typically demonstrate a χ²/df ratio below 3.0, RMSEA ≤ 0.08, and CFI and TLI ≥ 0.90. Accordingly, the CFA results confirm that the model adequately reflects the underlying latent constructs and their interrelationships. This provides empirical support for the structural soundness of the measurement model and affirms its suitability for subsequent structural equation modeling and hypothesis testing.

4.3. The Validity and Reliability

To ensure the robustness of the measurement model, we assessed construct reliability and validity using Cronbach’s alpha (α), Composite Reliability (CR), and Average Variance Extracted (AVE). Cronbach’s alpha was used to measure internal consistency, with values above 0.7 considered acceptable and those exceeding 0.8 indicating strong reliability. Additionally, Composite Reliability (CR), which accounts for factor loadings, was computed to further verify reliability. Following the guidelines proposed by Hair et al. [107], CR values exceeding 0.7 confirm the reliability of the constructs, ensuring that the observed variables consistently measure their intended theoretical dimensions.

To evaluate convergent validity, we employed Average Variance Extracted (AVE), where values greater than 0.5 indicate that a construct explains at least 50% of the variance in its observed indicators [108]. The results, as presented in

Table 2. Properties of the measurement model.

Construct	Description	Loading	Mean	SDs	CR	AVE
Inadequate sensing capabilities	Sensing1	0.778	3.87	0.655	0.84	0.52
	Sensing2	0.680	3.88	0.641
	Sensing3	0.758	3.86	0.660
	Sensing4	0.720	3.73	0.772
	Sensing5	0.653	3.90	0.618
	Cronbach’s α	0.840
Weak seizing capabilities	Seizing1	0.845	3.96	0.701	0.89	0.62
	Seizing2	0.613	3.92	0.726
	Seizing3	0.719	3.96	0.664
	Seizing4	0.862	4.00	0.677
	Seizing5	0.867	4.01	0.630
	Cronbach’s α	0.883
Poor reconfiguration capabilities	Reconfiguration1	0.763	4.03	0.612	0.88	0.56
	Reconfiguration2	0.575	4.00	0.631
	Reconfiguration3	0.750	4.03	0.585
	Reconfiguration4	0.736	4.05	0.627
	Reconfiguration5	0.851	4.04	0.622
	Reconfiguration6	0.772	3.99	0.647
	Cronbach’s α	0.878
Supply chain fragmentation	Fragmentation1	0.673	3.75	1.217	0.85	0.54
	Fragmentation2	0.727	3.46	1.247
	Fragmentation3	0.668	3.48	1.215
	Fragmentation4	0.723	3.73	1.072
	Fragmentation5	0.868	3.56	1.221
	Cronbach’s α	0.850
Limited ecosystem collaboration	Collaboration1	0.789	3.80	0.633	0.89	0.67
	Collaboration2	0.832	3.56	0.824
	Collaboration3	0.859	3.63	0.735
	Collaboration4	0.787	3.80	0.597
	Cronbach’s α	0.884
Lack of data analytics	Analytic1	0.911	3.35	0.719	0.89	0.68
	Analytic2	0.890	3.34	0.679
	Analytic3	0.734	3.31	0.699
	Analytic4	0.749	3.33	0.611
	Cronbach’s α	0.891
Digital transformation dead zone	Deadzone1	0.929	3.98	0.638	0.94	0.69
	Deadzone2	0.743	4.02	0.620
	Deadzone3	0.884	3.99	0.619
	Deadzone4	0.677	3.98	0.596
	Deadzone5	0.681	3.95	0.679
	Deadzone6	0.926	3.97	0.630
	Deadzone7	0.923	3.97	0.639
	Cronbach’s α	0.936

Note: Sensing = inadequate sensing capabilities, Seizing = weak seizing capabilities, Reconfiguration = poor reconfiguration capabilities, Fragmentation = supply chain fragmentation, Collaboration = limited ecosystem collaboration, Analytic = lack of data analytics, Deadzone = digital transformation dead zone, SD = standard deviation, CR = Composite Reliability, AVE = Average Variance Extracted.

, confirm that all constructs met the required reliability thresholds, with both Cronbach’s alpha and CR values exceeding 0.7. Furthermore, all AVE values were above 0.5, demonstrating convergent validity for each construct. The standardized factor loadings ranged from 0.575 to 0.929, further reinforcing the reliability of the measurement items. According to Cheung and Wang [109], constructs with explained variances greater than 50% are considered statistically valid, adding credibility to the methodological rigor of this study.

To assess discriminant validity, we applied the Fornell and Larcker [108] criterion, which states that the square root of each construct’s AVE should be greater than its correlations with other constructs. The findings confirmed that this condition was met, indicating that each construct is conceptually distinct and captures unique aspects of the data. This ensures that the measurement model effectively distinguishes between different theoretical constructs, minimizing concerns about construct redundancy. By meeting both convergent and discriminant validity criteria, the study establishes a strong and reliable measurement framework, providing a solid foundation for further empirical analysis.

4.4. Common Method Variance

Common Method Variance (CMV) refers to systematic error variation that arises when variables are measured using the same source or method, potentially introducing bias into the results [110,111]. This systematic error can lead to an overestimation or underestimation of relationships between variables, thereby affecting the accuracy of research findings [110,111]. One of the primary causes of CMV is consistent response patterns across survey items, which can distort the measurement of constructs and artificially inflate correlations between variables.

To mitigate the potential effects of CMV, several preventive measures were implemented in this study. First, the order of survey questions was randomized to reduce response bias, following the recommendations of Podsakoff et al. [112]. Randomizing question order minimizes the likelihood of patterned responses, thereby reducing systematic bias in participant answers. Additionally, Harman’s single-factor test was employed to detect the presence of CMV [112]. This test utilizes exploratory factor analysis (EFA) to determine whether a single factor accounts for most of the variance in the dataset. The results identified seven distinct factors with eigenvalues greater than 1.0, collectively explaining 68.607% of the total variance (

Table 3. The results of testing CMV.

	Initial Eigenvalues			Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %	Total
1	8.366	23.238	23.238	8.034	22.315	22.315	6.884
2	3.751	10.419	33.657	3.351	9.309	31.625	3.574
3	3.097	8.603	42.259	2.767	7.687	39.312	4.117
4	2.954	8.205	50.464	2.521	7.003	46.315	3.226
5	2.825	7.846	58.311	2.466	6.850	53.164	4.673
6	1.970	5.473	63.783	1.660	4.611	57.776	2.858
7	1.737	4.824	68.607	1.360	3.777	61.553	4.557
8	0.801	2.225	70.832
9	0.770	2.140	72.971

). Importantly, the first unrotated factor accounted for only 23.238% of the variance, which is significantly below the 40% threshold, indicating that CMV is not a significant concern in this study.

By implementing proactive methodological safeguards and conducting statistical tests to assess potential bias, this study ensures that CMV does not compromise the validity of the findings. These practices align with methodological strategies employed by Bhanot et al. [113], who similarly addressed CMV concerns in their investigation of digital readiness and sustainability practices. Their approach—combining procedural controls and post hoc statistical diagnostics—serves as a methodological parallel that reinforces the robustness and credibility of the current research design. These results confirm that the observed relationships among variables are likely to be accurate and unbiased, strengthening the credibility and reliability of the research conclusions.

4.5. Hypotheses Testing

As indicated in

Table 4. Means, SDs, and correlations.

Variables	1	2	3	4	5	6	7	8	9	10
1. Gender	1
2. Age	−0.068	1
3. Qualifications	−0.149 **	−0.113 **	1
4. Experience	−0.083 *	0.742 **	−0.032	1
5. Inadequate sensing capabilities	−0.005	0.070	−0.117 **	−0.028	1
6. Weak seizing capabilities	0.057	0.065	−0.082 *	0.046	0.208 **	1
7. Poor reconfiguration capabilities	0.002	0.037	−0.083 *	0.010	0.074	0.065	1
8. Supply chain fragmentation	0.057	−0.041	−0.089 *	−0.071	0.086 *	0.108 **	0.042	1
9. Limited ecosystem collaboration	0.077	−0.078	0.035	−0.072	0.025	0.043	−0.064	0.027	1
10. Lack of data analytics	0.006	−0.001	−0.114 **	−0.054	0.346 **	0.289 **	0.075	0.159 **	−0.019	1
11. Digital transformation dead zone	−0.008	0.074	−0.126 **	0.087 *	0.508 **	0.300 **	0.173 **	0.237 **	0.154 **	0.431 **

Note: ** = p < 0.01, * = p < 0.05.

, the correlation analysis reveals distinct relationships between various independent variables and the dependent variable, “Digital transformation dead zone.” Among these, inadequate sensing capabilities (β = 0.508, p < 0.01) exhibits the strongest positive correlation, suggesting that deficiencies in sensing market changes and technological advancements significantly contribute to digital stagnation. Similarly, a lack of data analytics (β = 0.431, p < 0.01) and weak seizing capabilities (β = 0.300, p < 0.01) also demonstrate notable positive correlations, indicating that poor data-driven decision-making and ineffective opportunity capture mechanisms exacerbate digital transformation challenges. Additionally, supply chain fragmentation (β = 0.237, p < 0.01) and limited ecosystem collaboration (β = 0.154, p < 0.01) present weaker yet still statistically significant, positive correlations, highlighting the role of disconnected supply chain structures and insufficient external partnerships in limiting digital progress. In contrast, qualifications (β = − 0.126, p < 0.01) and experience (β = 0.087, p < 0.05) show relatively weak correlations, suggesting that individual expertise alone may not be a decisive factor in overcoming digital stagnation. The findings underscore the critical influence of organizational capabilities—particularly in sensing, data analytics, and strategic execution—over structural and individual-level factors in determining digital transformation outcomes.

Structural equation modeling (SEM) is a robust statistical technique employed to examine complex relationships between observed (measured) and latent (unobserved) variables. By enabling the simultaneous assessment of multiple associations, SEM is particularly advantageous for evaluating theoretical models that involve multiple variables and indirect effects. In the present analysis, standardized coefficients were utilized to determine the direction and magnitude of external influences on endogenous variables, serving as the basis for hypothesis testing. The theoretical model demonstrated strong goodness-of-fit indices, with a chi-square value of 1262.926 (df = 568), a chi-square-to-degrees of freedom ratio of 2.223, and fit indices indicating a well-fitting model (CFI = 0.948, TLI = 0.942, GFI = 0.892, IFI = 0.948, and RMSEA = 0.046) (Figure 2). These results suggest that the proposed model adequately represents the relationships among the studied constructs, supporting its theoretical and empirical validity.

The results presented in

Table 5. Hypothesis testing results.

Hy.	Independent Variable	Dependent Variable	Beta	p-Value	Support Hypothesis
1	Inadequate sensing capabilities	Digital transformation dead zone	0.396	***	Yes
2	Weak seizing capabilities	Digital transformation dead zone	0.108	0.005	Yes
3	Poor reconfiguration capabilities	Digital transformation dead zone	0.115	0.001	Yes
4	Supply chain fragmentation	Digital transformation dead zone	0.158	***	Yes
5	Limited ecosystem collaboration	Digital transformation dead zone	0.168	***	Yes
6	Lack of data analytics	Digital transformation dead zone	0.220	***	Yes

Note: *** = p < 0.001, Hy. = Hypothesis.

offer clear empirical support for the study’s theoretical model, shedding light on the principal factors contributing to the digital transformation dead zone (DTDZ) in Vietnamese shipping SMEs. The analysis reveals that inadequate sensing capabilities (β = 0.396, p < 0.001) have the strongest impact, indicating that organizations struggling to detect and interpret digital opportunities are substantially more vulnerable to digital stagnation. This suggests that the inability to track evolving technological trends, shifting customer behaviors, and new regulatory requirements directly undermines firms’ strategic alignment—a challenge echoed in the work of Sreenivasan et al. [37], who emphasize digital foresight as a foundational driver of organizational resilience during crises.

Similarly, the significance of data analytics limitations (β = 0.220, p < 0.001) reinforces the vital role of evidence-based decision-making in driving transformation. Organizations lacking robust analytics frameworks are less capable of identifying optimization opportunities, forecasting trends, or quantifying risk—all essential in the maritime context where real-time decisions impact operational continuity. These findings confirm Hypotheses 1 and 6 and align with Qrunfleh et al. [39], who highlight how advanced analytics were crucial in maintaining supply chain agility during the COVID-19 pandemic.

External collaboration also emerged as a key dimension: limited ecosystem collaboration (β = 0.168, p < 0.001) and supply chain fragmentation (β = 0.158, p < 0.001) were both found to significantly contribute to the DTDZ. These findings underscore that digital transformation cannot occur in isolation; collaborative ecosystems and integrated logistics networks are essential enablers of systemic innovation. As shown by Fares et al. [114] in their study of fast-fashion MSMEs, poor ecosystem coordination leads to operational bottlenecks, demand misalignment, and missed opportunities to innovate—parallels that are especially relevant in shipping, where timely information sharing and platform integration (e.g., port community systems or API exchanges) are critical. These results support Hypotheses 4 and 5.

In comparison, poor reconfiguration capabilities (β = 0.115, p = 0.001) and weak seizing capabilities (β = 0.108, p = 0.005) were also statistically significant, albeit with smaller effect sizes. These findings suggest that while the ability to adapt internal structures and deploy resources strategically is important, it is less critical than upstream sensing and analytics. Nonetheless, these dimensions remain integral to long-term transformation success, particularly in later stages of digital maturity when agile reconfiguration becomes essential for scaling and innovation [11]. Thus, Hypotheses 2 and 3 are supported.

Taken together, the results point to a layered model of digital transformation inertia, where initial breakdowns in sensing and analytics cascade into broader structural and relational deficits. This supports the broader literature on dynamic capabilities, which emphasizes the sequential interplay between sensing, seizing, and transforming capabilities [34]. By contextualizing these findings within the Vietnamese maritime SME sector, the study highlights actionable levers—particularly investments in analytics infrastructure, inter-firm collaboration platforms, and early-stage sensing frameworks—as priorities for avoiding digital stagnation.

5. Discussion

Digital transformation has provided significant advantages for shipping companies, enhancing efficiency, operational agility, and data-driven decision-making. However, many firms struggle to fully leverage these benefits, often encountering barriers that lead to digital transformation dead zones. This study identifies key factors contributing to these challenges, offering valuable insights into the specific obstacles that hinder digital adoption in shipping companies. By highlighting these critical limitations, the findings contribute to a deeper understanding of the digital stagnation phenomenon and provide a foundation for developing strategic interventions to overcome transformation dead zones. Furthermore, the onset of the COVID-19 pandemic further intensified these challenges by exposing digital gaps and organizational unpreparedness across global supply chains. Studies such as Qrunfleh et al. [39] and Sreenivasan et al. [37] have emphasized that during the pandemic, firms with underdeveloped sensing and collaboration capabilities were disproportionately affected, unable to adjust to demand shocks and logistical disruptions. For shipping SMEs, this global crisis served as a stress test that revealed structural weaknesses, reinforcing the urgency of overcoming digital inertia.

The results indicate that inadequate sensing capabilities (β = 0.396, p < 0.001) exert the strongest influence on digital transformation stagnation. This finding is consistent with previous research highlighting the importance of sensing capabilities in dynamic business environments [115]. The inability to detect and interpret digital opportunities leads to strategic inertia, preventing firms from effectively adopting new technologies [116]. Similar studies suggest that firms that excel in environmental sensing are better positioned to leverage emerging technological trends and respond proactively to market shifts [117]. During COVID-19, the absence of real-time sensing severely impacted shipping firms’ ability to anticipate port delays, demand fluctuations, and policy changes. As documented by [31], this sensing failure directly contributed to stalled transformation efforts among SMEs.

In addition, the study finds that a lack of data analytics (β = 0.220, p < 0.001) is a significant contributor to digital stagnation. This result aligns with the work of Cao [118], who argues that digital transformation is increasingly reliant on data-driven decision-making. Without robust analytics, organizations struggle to extract actionable insights from digital systems, leading to suboptimal resource allocation and missed opportunities for innovation. However, as Holmström et al. [115] note, data availability alone is insufficient; firms must also develop the analytical capabilities necessary to convert raw data into strategic intelligence. The pandemic underscored this limitation. According to Bhanot et al. [113], SMEs that lacked digital analytics infrastructure were unable to adapt to sudden shifts in customer behavior or optimize logistics under pandemic constraints—leading to what Varma and Dutta [32] describe as “digital disorientation” among early-stage adopters.

Furthermore, limited ecosystem collaboration (β = 0.168, p < 0.001) and supply chain fragmentation (β = 0.158, p < 0.001) were found to significantly hinder digital transformation. These findings are in line with previous studies suggesting that fragmented supply chains and weak inter-organizational collaboration create significant barriers to digital integration [119]. The role of supply chain networks in digital transformation has been widely studied, with research emphasizing that higher levels of connectivity and collaboration enhance firms’ ability to integrate digital solutions across operational processes [120]. Additionally, Hasan [121] highlights that fragmented ecosystems reduce firms’ ability to coordinate digital initiatives effectively, further reinforcing the necessity of a well-integrated supply chain infrastructure. As Fares et al. [114] illustrate in the fast-fashion sector, pandemic-era supply chain fragility mirrored the conditions faced by maritime SMEs—where disrupted port operations and siloed communication systems severely restricted collaborative innovation. These parallels suggest that fostering ecosystem resilience is not merely a strategic choice but a critical survival mechanism under volatile conditions.

Comparatively, poor reconfiguration capabilities (β = 0.115, p = 0.001) and weak seizing capabilities (β = 0.108, p = 0.005) also contribute to digital transformation dead zones but with weaker effects. These results are consistent with Wójcik et al. [122], who suggest that while the ability to reconfigure resources is critical for long-term digital adaptation, it may not be as immediate a barrier as sensing and data analytics deficiencies. Similarly, prior studies have indicated that firms with strong dynamic capabilities can better adapt to technological disruptions, but without an initial foundation in sensing and analytics, reconfiguration alone may not be sufficient [123]. Jiao et al. [124] also note that reconfiguration capabilities become more relevant in later stages of digital transformation, once fundamental challenges such as sensing and data utilization have been addressed.

Overall, these results emphasize that while all tested factors contribute to digital transformation dead zones, the most influential barriers appear to be deficiencies in sensing and data analytics capabilities, followed by collaboration and supply chain issues, with adaptive and reconfiguration capabilities playing a comparatively smaller yet still significant role. This aligns with broader digital transformation literature, which suggests that successful digitalization is contingent on firms’ ability to perceive market shifts, analyze complex datasets, and foster collaborative innovation within their ecosystems [125]. Future research should explore additional moderating variables, such as organizational culture and technological readiness, to better understand the interplay between these factors and digital transformation outcomes.

6. Theoretical and Practical Implications

This study advances a theoretical understanding of digital transformation dead zones by integrating insights from the dynamic capabilities theory [91], the resource-based view (RBV) [126], and the ecosystem theory [23]. The findings highlight sensing capabilities, data analytics, ecosystem collaboration, and supply chain fragmentation as critical determinants of digital stagnation. Inadequate sensing capabilities emerged as the most significant barrier, reinforcing the dynamic capabilities theory, which posits that firms must sense, seize, and transform resources to remain competitive [91]. Organizations lacking strong sensing capabilities struggle to identify technological shifts and market trends, leading to strategic inertia and stalled digital adoption [127]. Additionally, the study confirms that deficiencies in data analytics significantly contribute to digital stagnation, supporting RBV, which underscores the necessity of data-driven decision-making for competitive advantage [126]. The inability to leverage real-time data and predictive insights limits operational efficiency and prevents firms from effectively navigating digital transformation [128,129]. Furthermore, limited ecosystem collaboration and supply chain fragmentation serve as substantial barriers, aligning with the ecosystem theory, which suggests that digital transformation is an interdependent process requiring coordinated efforts across stakeholders [23]. Fragmented supply chains and weak collaboration hinder digital integration by creating misaligned incentives, inconsistent data-sharing practices, and resistance to change [130]. The study also supports the digital platform theory, emphasizing the need for firms to transition toward integrated digital ecosystems to maximize transformation benefits [131]. While poor reconfiguration and weak seizing capabilities also contribute to digital stagnation, their influence is comparatively lower. These findings align with organizational adaptation theories, suggesting that reconfiguration plays a secondary role in transformation, becoming more relevant once sensing and data utilization challenges are addressed [124,132]. Overall, the study provides empirical validation for a multi-stage digital transformation framework, prioritizing sensing and analytics as foundational enablers before addressing collaboration, supply chain integration, and structural adaptation.

The findings of this study provide critical managerial insights into overcoming digital transformation barriers by emphasizing the importance of sensing capabilities, data analytics, ecosystem collaboration, and supply chain integration. Organizations must enhance sensing capabilities by investing in AI-driven market intelligence and digital literacy training to detect and capitalize on emerging opportunities [129]. Strengthening data analytics capabilities through predictive analytics, machine learning, and AI-driven decision-making systems is essential for operational efficiency and strategic agility [128]. Additionally, fostering ecosystem collaboration by establishing strategic alliances and shared digital platforms can facilitate cross-industry digital integration, reducing digital silos and enhancing technological adoption [130]. Addressing supply chain fragmentation requires IoT, blockchain, and cloud-based systems to enhance transparency and efficiency across supply networks [23]. Furthermore, organizations must develop adaptive and reconfiguration capabilities by adopting agile management frameworks and modular digital architectures to maintain long-term digital resilience [132]. From a policy perspective, government bodies and industry regulators should consider establishing public–private digital innovation hubs to assist SMEs with access to advanced technologies and skill-building initiatives. Financial subsidies and tax incentives for technology adoption can help offset the cost barriers that often deter small firms from engaging in sustained digital transformation. Additionally, mandating interoperability standards across maritime digital platforms would facilitate smoother integration of SMEs into broader supply chain ecosystems.

Managerially, firms should adopt digital transformation roadmaps that begin with low-cost sensing and analytics enhancements before advancing to higher-risk investments in digital reconfiguration. Leadership development programs focused on digital change management and cross-functional collaboration should be implemented to ensure organizational readiness. Regular diagnostic assessments of digital maturity can also guide investment decisions and identify early signs of digital stagnation. These insights underscore the need for a strategic, multi-stage approach to digital transformation, prioritizing foundational enablers such as sensing and analytics before addressing broader structural and ecosystem integration challenges. Future research should explore the role of regulatory frameworks and emerging technologies in optimizing digital transformation strategies and mitigating stagnation risks [131].

7. Limitations and Future Directions

Despite the valuable contributions of this study, several limitations must be acknowledged. First, its focus on small- and medium-sized enterprises (SMEs) in the shipping industry limits the generalizability of the findings to larger shipping corporations or other sectors undergoing digital transformation. Larger firms typically possess more substantial financial and technological resources, which may modulate the impact of sensing capabilities, data analytics, and ecosystem collaboration on transformation outcomes [91]. Future studies should explore whether these identified barriers hold similar significance in large enterprises and in industries at various levels of digital maturity.

Second, the study adopts a cross-sectional design, capturing digital transformation dynamics at a single point in time. Given that digital transformation is inherently continuous and iterative—shaped by evolving technologies, shifting regulatory frameworks, and fluctuating market dynamics—future research should adopt longitudinal designs to investigate how firms enter, navigate, or escape digital transformation dead zones over time. As Gupta and Kumar Singh [31] demonstrate using a temporal lens to study MSME recovery post-COVID-19, examining the trajectories of digital transformation across different phases of the pandemic could yield critical insights into resilience-building and adaptation patterns.

Third, while the quantitative methodology employed in this study offers empirical clarity regarding key relationships, it may not fully capture the contextual nuances of organizational decision-making, leadership influence, and cultural factors. Future research should incorporate qualitative approaches—such as in-depth case studies, semi-structured interviews, or ethnographic methods—to provide richer, firm-level narratives about transformation successes and failures. A mixed-method approach would allow scholars to triangulate statistical results with experiential insights, offering a more holistic understanding of digital transformation challenges.

Moreover, expanding research to include comparative analysis across regional maritime hubs—such as Singapore, Rotterdam, or Busan—could provide critical insights into how geographical, infrastructural, and policy-specific variables influence digital transformation outcomes. Cross-country studies may uncover regionally tailored barriers and accelerators, revealing how institutional settings and local ecosystems impact firms’ digital readiness and transformation strategies.

Additionally, the study primarily examines organizational and technological barriers but does not fully account for macro-environmental factors such as industry regulations, government incentives, and geopolitical uncertainties. Future studies should integrate these external influences to better understand how policy environments constrain or facilitate digital transformation in shipping. Assessing the role of pandemic-triggered interventions—such as digital subsidy programs, port modernization projects, or trade digitalization policies—could offer actionable insights for governments and industry bodies.

Lastly, the current model does not investigate potential moderating or mediating variables—such as organizational culture, leadership commitment, and digital readiness—that may influence the strength or direction of identified relationships. Future studies should apply moderation and mediation analysis to reveal interaction effects, enabling the development of predictive frameworks that identify under which conditions digital transformation initiatives are most likely to succeed.

Addressing the limitations of the convenience sampling method also remains essential. Future research should ensure greater transparency in sample selection and explore the use of stratified or purposive sampling to enhance representativeness across organizational sizes, roles, and geographies. Cross-referencing similar sampling strategies, such as those used by Bhanot et al. [113], would further strengthen methodological rigor and academic credibility.

In summary, future research should embrace a broader scope by investigating diverse industries and geographies, applying longitudinal and mixed-method approaches, and incorporating environmental and behavioral factors that shape digital transformation. These enhancements will provide deeper theoretical insights and practical guidance for navigating digital dead zones in the post-pandemic economy.

8. Conclusions

This study identifies sensing capabilities and data analytics as primary barriers to digital transformation among shipping SMEs, followed by ecosystem collaboration and supply chain fragmentation. These findings extend the dynamic capabilities theory, the resource-based view, and the ecosystem theory by highlighting foundational digital deficiencies that create transformation dead zones.

However, the conclusions are limited by the study’s cross-sectional design and focus on Vietnamese SMEs, which may not capture broader industry patterns or temporal dynamics. To reflect actual conditions more accurately, future research should adopt longitudinal and mixed-method approaches to examine digital transformation trajectories over time and across different regional maritime hubs, as proposed by Gupta and Kumar Singh [31]. Incorporating macro-environmental factors and organizational moderators—such as leadership, culture, and digital readiness—will offer a more holistic understanding of transformation barriers. These enhancements are vital for crafting adaptive, resilient digital strategies in the evolving maritime sector.