Digital Transformation and Business Model Innovation: Enhancing Productivity in the Croatian Maritime Transport Sector

Abstract

This research investigates how business model changes induced by digital transformation impact productivity within the maritime transport sector in Croatia. Given the limited existing literature addressing digital transformation’s productivity implications, specifically in maritime contexts, this study aims to identify and analyze key mediating factors. An online survey conducted among Croatian maritime transport stakeholders resulted in 82 valid responses, which were statistically analyzed using descriptive statistics, Spearman’s correlation, and principal component analysis (PCA). The study identifies two primary dimensions of business model changes—innovation and process digitalization—that significantly correlate with increased productivity. Key influencing factors include the digitalization of internal and external business processes, development of new digital revenue streams, introduction of innovative services, and novel pricing models. Results underscore the importance of targeted digital transformation initiatives and serve as a valuable reference for maritime transport stakeholders, aiming to enhance their productivity and competitiveness through digital innovation.

1. Introduction

The maritime sector in Croatia plays a vital strategic and economic role, reflecting the country’s extensive Adriatic coastline, rich maritime heritage, and integration into broader European logistics networks. It encompasses a wide range of stakeholders, including state maritime administrations, port authorities, shipping companies, freight forwarders, sea brokers, terminal operators, and supporting hinterland transport providers, such as road and rail carriers. Croatia’s major seaports—such as Rijeka, Split, and Ploče—serve as critical nodes not only for national trade flows but also for trans-European transport corridors, particularly Corridor Vb, linking Central Europe with the Adriatic Sea. However, in comparison to more digitally mature Western European ports like Rotterdam, Hamburg, or Antwerp, Croatian maritime logistics exhibit several structural and technological gaps. These include fragmented information systems, the reliance on manual procedures, limited interoperability between stakeholders, and slower adoption of advanced digital platforms, such as port community systems (PCSs), Internet of Things (IoT)-enabled monitoring, and AI-based predictive analytics. The institutional and regulatory inertia, lack of investment in digital infrastructure, and a general organizational conservatism in public and private maritime actors have historically constrained innovation. Nevertheless, recent years have witnessed a growing momentum in digital transformation efforts driven by EU funding, national strategic frameworks (e.g., Smart Specialisation Strategy), and pressures to align with the European Green Deal and Fit-for-55 initiatives. Notably, Croatian maritime stakeholders are increasingly recognizing the productivity and efficiency gains available through the digitalization of business processes, integration of multimodal logistics platforms, and deployment of innovative business models. The emergence of “smart port” initiatives, albeit at early stages, mirrors European trends towards data-driven, sustainable, and customer-centric logistics operations. Yet, challenges remain in harmonizing stakeholder collaboration, developing digital skills within the maritime workforce, and securing resilient digital infrastructures. As a result, Croatia finds itself at a critical crossroads: while it possesses the geographical advantages and policy incentives to elevate its maritime sector into a competitive regional logistics hub, it must overcome systemic barriers and adopt a holistic, innovation-led approach to fully capitalize on the opportunities of digital transformation in line with broader European maritime logistics evolution.

The transformative potential of digital technologies in reshaping organizational productivity and the competitive advantage is gaining increasing attention from researchers and industry practitioners [1,2]. Digital transformation encompasses more than merely adopting new technologies; it also involves fundamentally rethinking business models, processes, and corporate strategies to adapt to evolving market demands and consumer expectations [3,4]. While numerous industries have rapidly integrated digital advancements, the maritime transport sector faces unique challenges, causing slower progress in digital transformation compared to other transportation sectors [3,5]. Maritime transport stakeholders often experience inefficiencies due to traditional manual processes, insufficient coordination among stakeholders, and the inadequate integration of digital solutions [6,7]. These inefficiencies lead to increased operational costs, reduced reliability, and longer transit times [5]. Addressing these challenges through targeted digital initiatives, such as the digitalization of internal and external processes and the adoption of innovative business models, is essential for enhancing productivity and operational efficiency within maritime transport stakeholders [1,3,6].

The maritime sector’s digital transformation journey involves significant organizational and cultural changes, requiring new business models, advanced digital solutions, and comprehensive integration strategies. These transformations streamline cargo handling, improve document management, enhance financial transaction efficiency, and optimize overall operational processes [5,6,7]. However, adopting digital practices in maritime transport stakeholders is hindered by several barriers, including entrenched organizational cultures, limited resource allocation, and regulatory complexities [3,4]. Additionally, challenges such as cybersecurity threats and fragmented regulations further complicate the transition to digital processes, potentially stifling innovation [6,7]. For decision-makers in enterprises and governments, it is important to understand how enterprises are approaching the adoption of digital technologies and how successful they are in the digital transformation journey. This paper builds on the existing body of literature on digital transformation within maritime sectors, particularly utilizing the Technology–Organization–Environment (TOE) framework introduced by Tornatzky and Fleischer (1990) [8], which examines technological adoption, organizational preparedness, and external environmental influences as key determinants in successful digital transformation initiatives [1,7].

The aim of our research is to investigate how digital transformation influences business model changes and productivity within Croatian maritime transport stakeholders.

2. Methodology

The research is structured in the following way, as shown in the Figure 1.

2.1. Literature Review

To ensure the inclusion of the most recent findings, the literature review was initially focused on literature published within the past three years. This timeframe ensures that the review captures the latest developments and trends, reflecting the dynamic nature of the sector’s transformation. This review utilized the Web of Science database, employing specific search terms to ensure a focused retrieval of relevant literature. The Web of Science database was considered as it represents the world’s leading scientific citation search and analytical information platform [9].

The keywords used in the search included the following:

• Digital Transformation AND Maritime Transport
• Digital Transformation AND Seaport
• Digital Transformation AND Shipping

Recognizing the strategic role of seaports as pivotal nodes in the transport chain [10] and acknowledging that their competitiveness and development are strongly influenced by connectivity with the hinterland [11,12], the authors also included rail and road carriers in both the survey and the literature search. These actors play a crucial role in the functioning of maritime logistics chains and were thus relevant for a holistic understanding of digital transformation impacts across the transport ecosystem.

The search criteria within the Web of Science database were limited to either the topic or title of the articles, and only those published in the English language were considered. Subsequently, manual screening of the papers was undertaken to assess their relevance to the topic at hand.

The statistical part of this study builds upon acquired data and research findings by Jović et al. [3], which encompasses various factors related to digital transformation initiatives and productivity outcomes in maritime transport stakeholders.

This study further explores what are the digital transformation business model changes key factors to increase productivity in maritime transport stakeholders. For the purpose of this study, an online survey was conducted among 262 maritime transport stakeholders in Croatia that were invited to participate in the research. In total, 94 organizations responded to the survey. Out of the 94 that filled out questionnaires, 82 questionnaires were usable for analysis in this study, while 12 were incomplete.

The questionnaire consisted of 51 questions, out of which the initial 7 were demographics questions, followed by 30 questions related to the technological, organizational, and environmental factors (TOE framework) of digital transformation. The second part of the questionnaire consisted of 8 questions about digital transformation business model changes (Table 1) and 6 questions about digital transformation, which also included the question about the increase in productivity due to digital transformation (Table 1).

A questionnaire was used to measure two main underlying constructs for this study, namely the following:

• Digital transformation business model changes (BMCs);
• The productivity increase (P).

A focused literature review was conducted to deepen the understanding of the relationship among digital transformation, business model characteristics, and productivity in the maritime transport sector.

Sooprayen et al. [13] provided a structured framework identifying 17 key factors influencing innovation adoption in ports, including the organizational support, financial capacity, network embeddedness, and perceived usefulness. These factors conceptually align with variables examined in this study, particularly collaboration with partners (BMC1), internal and external process digitalization (BMC2, BMC3), and the perceived impact of digital transformation on productivity (PROD).

Kaczerska et al. [14] investigated the adoption of digital tools in ferry shipping, focusing on innovations such as online ticket sales, chatbot integration, and social media engagement. These initiatives represent a shift towards new service delivery models and customer interaction channels, aligning directly with variables such as new services (BMC6), new sales channels (BMC7), and new charging methods (BMC8). Moreover, the strategic use of external platforms and digital interfaces implies cross-organizational collaboration (BMC1).

Pang et al. [15] investigated the impact of digital transformation on sustainable management performance in shipping firms, focusing on the moderating role of coordination mechanisms (both cross-functional and customer-based) and social norms. Although their primary outcome is sustainability, their study implicitly addresses efficiency and process performance improvements—concepts closely related to productivity (PROD). The findings are particularly relevant to this research, as they reinforce the idea that the effectiveness of digital transformation depends not only on technology adoption, but also on internal organizational capabilities (BMC2) and stakeholder collaboration (BMC1).

Belmoukari et al. [16] provided a comprehensive systematic literature review of the smart port concept, emphasizing digital transformation as a multidimensional shift involving technological innovation, stakeholder collaboration, and operational efficiency. Their classification of smart port features—such as real-time information sharing, intelligent infrastructure, and process automation—aligns closely with several business model change variables in this research (e.g., internal/external process digitalization—BMC2, BMC3; new services—BMC6; new revenue streams—BMC4). Notably, they identify operational efficiency and productivity improvement as key goals of smart port transformation, reinforcing the validity of using productivity (PROD) as a performance indicator in evaluating the outcomes of digital transformation initiatives in maritime transport stakeholders.

Guerrero-Molina et al. [17] provided a scientometric review of Industry 4.0 technologies in port operations, highlighting key development trajectories, such as smart port development, sustainability, and digital innovation. Their findings emphasize how technologies such as IoT, AI, and digital twins are transforming port processes and value chains, enhancing productivity, automation, and stakeholder integration. These insights align closely with the variables analyzed in this research—particularly internal and external process digitalization (BMC2, BMC3), the introduction of new services (BMC6), and increased productivity as a performance outcome (PROD).

Basulo-Ribeiro and Teixeira [18] presented a systematic literature review on the application of Industry 4.0 technologies in seaport logistics, emphasizing their role in improving operational efficiency, stakeholder collaboration, and sustainability. Their findings support several key aspects analyzed in this study, particularly the digitalization of internal and external processes (BMC2, BMC3), cooperation with partners in digital development (BMC1), and the enhancement of productivity (PROD). The review also highlights benefits such as real-time operations and improved and personalized customer service, aligning with the examined variables on innovative service provision and revenue generation (BMC4, BMC6, BMC8).

Durán et al. [19] developed the DMLBC method—an integrated system combining blockchain and machine learning—to improve management control and decision-making in Chilean seaports. Their model demonstrates how digital transformation can increase operational efficiency through real-time monitoring, the automation of documentation, and data-driven forecasting. These aspects strongly align with the variables in this research, particularly in relation to internal and external process digitalization (BMC2, BMC3), productivity gains (PROD), and enhanced stakeholder collaboration (BMC1). Furthermore, the study highlights the potential of smart contracts and predictive analytics to support new service delivery and revenue models (BMC4, BMC6, BMC8), reinforcing the role of innovation as a key driver of digital transformation in maritime logistics.

Nguyen [1] used the Technology–Organization–Environment (TOE) framework to identify key drivers and outcomes of digital transformation in Vietnamese shipping companies. The study finds that digital transformation, supported by digital technology development, skills, and top management commitment, significantly improves economic performance—aligning with this research’s findings on productivity (PROD). Moreover, Nguyen emphasizes digital interconnectivity, process automation, and real-time data sharing, which resonate with the analyzed variables related to internal and external process digitalization (BMC2, BMC3). The study also confirms that digital transformation facilitates new business models and services, linking conceptually to variables concerning innovative services, revenue streams, and pricing models (BMC4, BMC6, BMC8). These insights confirm that digital innovation plays an important role in improving productivity in maritime transport stakeholders.

Gao et al. [20] explored how a digital platform in container shipping—Duckbill Technology—built digital resilience during the COVID-19 crisis through evolving orchestration roles. Their longitudinal case study highlights how digital transformation supported internal and external process digitalization (BMC2, BMC3), stakeholder collaboration (BMC1), and the development of new services, pricing models, and market expansion (BMC4–BMC8). By applying dynamic strategies such as activation, buffering, and leapfrogging, the company managed to enhance operational flexibility and efficiency, ultimately increasing productivity (PROD).

Yang and Lin [21] investigated how digitalization and digital logistics platform adoption affect digital transformation and organizational performance in Taiwan’s maritime logistics sector. Their results show that both digitalization and digital transformation significantly enhance organizational performance, including productivity enhancement, value creation, and customer interaction—which closely align with variables used in this research (e.g., productivity improvement—PROD, new revenue streams—BMC4, new services—BMC6). Notably, while platform adoption alone had no direct effect on performance, its positive impact was fully mediated by internal digital transformation, highlighting the importance of digitalizing internal processes (BMC2) before engaging in broader collaborative innovations (BMC1).

Yu et al. [22] evaluated the competitiveness of container shipping companies in the era of sustainability and digitalization, emphasizing the critical role of technological innovation, customer satisfaction, and the service scope. Their operational framework incorporates both direct and indirect performance indicators—such as the digitalization level, environmental protection, and service reach—which conceptually align with the constructs used in this study (e.g., internal and external process digitalization, new service development, and cooperation with partners). Their findings suggest that higher levels of digital maturity contribute to enhanced operational performance, which resonates with this study’s observation that changes in the digital transformation business model are positively associated with a productivity increase (PROD).

Bucak [23] highlighted digital transformation as the most influential trend in the liner shipping sector, emphasizing its role in gaining a competitive advantage. This supports the findings of the present study, where business model changes such as process digitalization (BMC2, BMC3), new services (BMC6), and pricing models (BMC8) are linked to productivity improvements (PROD). The study reinforces the idea that digital adaptation enhances both operational efficiency and market positioning in maritime logistics.

Lam and Tang [24] analyzed the implementation of robotic process automation (RPA) in the cold chain management of a freight forwarding company. Their case study demonstrates significant improvements in operational efficiency and data processing speeds—up to 97% time savings—after adopting RPA bots for shipment and temperature tracking. These results align closely with the internal process digitalization variable (BMC2) and support the observed positive effect of digital transformation on productivity (PROD) in this study. Moreover, the improved workflow and automated reporting enhance service delivery, corresponding to business model innovations such as new services (BMC6).

Aerts and Mathys [25] analyzed digitalization trends in maritime transport based on over 500 industry presentations. They identify key focus areas such as smart ports, AI, blockchain, and digital supply chains. These insights support this study’s focus on internal and external process digitalization (BMC2, BMC3) and suggest that digital innovation contributes to improved operational performance, aligning with the observed relationship between business model changes and productivity (PROD).

Evmides et al. [26] developed advanced machine-learning-based models to improve the accuracy of vessel arrival time predictions using AIS data in the Eastern Mediterranean. By enhancing ETA reliability, the study demonstrates tangible benefits in resource planning and port efficiency. These operational improvements align closely with the business model change variables in this study—especially the digitalization of internal and external processes (BMC2, BMC3)—and support the notion that targeted digital initiatives contribute to productivity gains (PROD) in maritime transport stakeholders.

Su et al. [27] highlighted the deployment of advanced digital technologies—such as digital twin systems, blockchain, and automated customs clearance—as enablers of service innovation within port operations. While not explicitly labeled as “new services”, these tools represent enhanced service offerings aligned with the construct of BMC6, showcasing how digital transformation facilitates value-added services in the maritime domain. The emphasis on efficiency also indirectly supports the link with productivity improvements (PROD).

Gündoğan and Keçeci [28] explored digital transformation adoption in maritime logistics using the Technology Acceptance Model. The findings of the study indicate that the degree to which employees in the industry embraced digital transformation technologies was influenced by various factors, including their perception of how easy these technologies were to use and the perceived benefits they offered. Their findings therefore provide additional insight into the organizational dynamics that can drive or hinder productivity (PROD) improvements in maritime logistics.

Melnyk et al. [29] proposed a probabilistic model for assessing cybersecurity risks in shipboard systems, emphasizing the increasing vulnerability of digitalized maritime assets. While the study is primarily technical and focuses on risk modeling, its insights are relevant to the broader context of digital transformation in the maritime sector. Secure and resilient digital systems are a prerequisite for the successful implementation of internal and external process digitalization (BMC2, BMC3), as well as for sustaining productivity gains (PROD) and developing trustworthy digital service offerings (BMC6). Their findings underscore the need to consider cybersecurity as a strategic component of digital business model changes.

2.2. Research Questions

Based on this theoretical foundation, the current study aims to empirically investigate how digital transformation influences business model changes and productivity within Croatian maritime transport stakeholders. Employing a survey methodology, this study examines the state of digital transformation, categorizing influencing factors according to the TOE framework and assessing their impacts on productivity outcomes. Croatia serves as an appropriate context for this research due to its strategic geographic location, significant role in European maritime logistics networks, and increasing investments in digital transformation initiatives, particularly within its major ports. This research addresses two central questions:

• How do changes in business models driven by digital transformation influence productivity in Croatian maritime transport stakeholders?
• What specific factors mediate the relationship between digital transformation-driven business model changes and productivity?

The results from this study provide valuable insights into the key factors facilitating digital transformation, contributing to the global understanding of digital innovation’s role in maritime sector productivity. These findings can assist stakeholders and decision-makers in designing effective digital strategies in order to increase productivity in their respective organizations.

TOE mapping will clearly demonstrate that the research aligns its measured variables directly with each dimension of the TOE framework, supporting a comprehensive analysis and reliable conclusions about the relationship among digital transformation, business model innovation, and productivity within the maritime transport sector. In case of our research, TOE mapping is done as follows:

• Technological dimension (BMC2, BMC3): Digitalization processes directly involve the implementation and usage of technological systems, solutions, or infrastructures.
• Organizational dimension (BMC1, BMC4, BMC6, BMC7, BMC8): These variables reflect internal strategic, managerial, or innovative capabilities essential for effectively adopting and utilizing digital transformation.
• Environmental dimension (BMC5): Focuses explicitly on external market orientation, competition, and environmental pressures.

3. Results

The BMC construct consisted of eight questions and the P construct of one question. The scale had a high level of internal consistency, as determined by a Cronbach’s alpha of 0.904. A Cronbach’s alpha value equal or higher than 0.7 shows a good level of internal consistency [30].

For the analysis of gathered data, the SPSS 29 software package was used. The analysis included the following:

• Descriptive Statistics: To summarize the data characteristics and establish a foundational understanding of the dataset;
• Correlation Analysis: To identify potential relationships between the variables associated with business model changes and productivity increases, the Spearman rho coefficient analysis was used due to the use of an ordinal Likert scale (1-totally disagree, 5-totally agree). All variables had paired observations (N = 82), and a test of monotonic relationships among variables was conducted based on a scatter plot diagram in SPSS;
• Principal Component Analysis (PCA): To reduce dimensionality and identify the underlying structures influencing productivity stemming from digital transformation efforts. PCA requires 5–10 cases per variable. Since for the purpose of this research study PCA was run on eight variables, our sample size N = 82 was of a sufficient size (N > 80).

Table 1 presents the descriptive statistics of the variables, which were analyzed in this research study.

Table 1. Variable short and long name.

Short Name	Variable Name	N	Mean	Std. Deviation
BMC1	The organization cooperates with new partners with the aim of developing new digital solutions	82	3.46	1.091
BMC2	The organization has digitalized internal business processes	82	3.70	1.039
BMC3	The organization has digitalized external business processes	82	3.37	1.025
BMC4	The organization generates additional revenue from new sources as a result of the implementation of digital technologies	82	2.79	1.108
BMC5	The organization has entered new markets as a result of digitalization and digital transformation	82	2.60	1.076
BMC6	The organization provides new services as a result of digitalization and digital transformation	82	3.01	1.232
BMC7	The organization has introduced new sales channels as a result of digitalization and digital transformation	82	2.90	1.273
BMC8	The organization has introduced new ways of charging for services as a result of digitalization and digital transformation	82	3.02	1.296
PROD	The organization has increased productivity by introducing digital transformation	82	3.54	0.905

Table 1. Variable short and long name.

Short Name	Variable Name	N	Mean	Std. Deviation
BMC1	The organization cooperates with new partners with the aim of developing new digital solutions	82	3.46	1.091
BMC2	The organization has digitalized internal business processes	82	3.70	1.039
BMC3	The organization has digitalized external business processes	82	3.37	1.025
BMC4	The organization generates additional revenue from new sources as a result of the implementation of digital technologies	82	2.79	1.108
BMC5	The organization has entered new markets as a result of digitalization and digital transformation	82	2.60	1.076
BMC6	The organization provides new services as a result of digitalization and digital transformation	82	3.01	1.232
BMC7	The organization has introduced new sales channels as a result of digitalization and digital transformation	82	2.90	1.273
BMC8	The organization has introduced new ways of charging for services as a result of digitalization and digital transformation	82	3.02	1.296
PROD	The organization has increased productivity by introducing digital transformation	82	3.54	0.905

The survey participants were from small, medium-sized, and large maritime transport stakeholders of various types as shown in

Table 2. Survey participants’ organization sizes.

	N	%
No answer	1	1.2%
Small organization (up to 49 employees)	55	67.1%
Medium organization (50–249 employees)	14	17.1%
Large organization (250+ employees)	12	14.6%

. Most participants, 67.1%, were from small organizations with up to 49 employees; 17.1% of respondents were from medium-sized organizations with 50–249 employees, while 14.6% were from large organizations that have 250 or more employees.

The largest group of organizations were state maritime transport organizations and administration (25.6%), followed by shipping and logistics stakeholders (each account for 14.6%), sea brokers (13.4%), port operators and terminals, along with the ‘Other’ category, each comprising 11.0%. The remaining participants were involved in road transport, 3.7%, seaport agents, 3.7%, and railways transport, 1.2% (

Table 3. Survey participant organization type.

	N	%
State organization and administration	21	25.6%
Shipping	12	14.6%
Road transport	3	3.7%
Railways transport	1	1.2%
Sea port agent	3	3.7%
Sea brokers	11	13.4%
Logistics operators	12	14.6%
Port operators and terminals	9	11.0%
Other	9	11.0%
No answer	1	1.2%

).

Most respondents held a position of executive directors (35.4%), followed by IT executive directors (9.8%) and project managers cover (7.3%); 4.9% of participants were sales executive directors. A small percentage of respondents did not provide an answer to this question (2.4%) (

Table 4. Respondent function in organizations.

	N	%
Executive director	29	35.4%
IT executive director	8	9.8%
Project manager	6	7.3%
Sales executive director	4	4.9%
No answer	2	2.4%
Other	33	40.2%

).

A Spearman’s rank-order correlation was run (

Table 5. Spearman rho correlation coefficient between digital transformation business model changes and productivity increases.

			PROD	BMC1	BMC2	BMC3	BMC4	BMC5	BMC6	BMC7
Spearman’s rho	BMC1	Corr. Coef.	0.200
		Sig. (2-tailed)	0.072
	BMC2	Corr. Coef.	0.487 **	0.476 **
		Sig. (2-tailed)	<0.001	<0.001
	BMC3	Corr. Coef.	0.373 **	0.466 **	0.729 **
		Sig. (2-tailed)	<0.001	<0.001	<0.001
	BMC4	Corr. Coef.	0.333 **	0.342 **	0.343 **	0.394 **
		Sig. (2-tailed)	0.002	0.002	0.002	<0.001
	BMC5	Corr. Coef.	0.186	0.352 **	0.271	0.282	0.755 **
		Sig. (2-tailed)	0.094	0.001	0.014	0.010	<0.001
	BMC6	Corr. Coef.	0.322 **	0.391 **	0.392 **	0.413 **	0.715 **	0.741 **
		Sig. (2-tailed)	0.003	<0.001	<0.001	<0.001	<0.001	<0.001
	BMC7	Corr. Coef.	0.230	0.345 **	0.322 **	0.312 **	0.740 **	0.621 **	0.690 **
		Sig. (2-tailed)	0.038	0.001	0.003	0.004	<0.001	<0.001	<0.001
	BMC8	Corr. Coef.	0.306 **	0.421 **	0.416 **	0.468 **	0.627 **	0.535 **	0.569 **	0.668 **
		Sig. (2-tailed)	0.005	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001

** Correlation is significant at the 0.01 level (2-tailed). N = 82.

) to assess the relationship between digital transformation business model changes and the productivity increase. Eighty-two survey participants provided valid responses that were included in this analysis. The preliminary analysis showed the relationship to be monotonic, as evidenced by the visual inspection of a scatterplot, which enabled further analysis.

There was a statistically significant, strong positive correlation between the digitalization of internal business processes (BMC2) and a productivity increase (PROD), rs = 0.487, p < 0.01. Furthermore, there was also a similar statistically significant, strong positive correlation between the digitalization of external business processes (BMC3) and a productivity increase (PROD), rs = 0.373, p < 0.01. Additionally, there was also a statistically significant, strong positive correlation between generating additional revenue from new sources as a result of the implementation of digital technologies (BMC4) and an increase in productivity (PROD), rs = 0.333, p < 0.01; between providing new services as a result of digital transformation (BMC6) and an increase of productivity (PROD), rs = 0.322, p < 0.01; and between introducing new ways of charging for services as a result of digital transformation (BMC8) and an increase of productivity (PROD), rs = 0.306, p < 0.01.

Altogether, we can sum up that five different variables for digital transformation business model changes are statistically significantly correlated with the increase in productivity. Additionally, it is also noticeable from Table 5 that there are many statistically significant correlations among other digital transformation business model change variables, which were taken into consideration for further principal components analysis when selecting appropriate method for rotating components.

Since there are many statistically significant correlated variables, we performed principal component analysis in order to extract digital transformation business model change key factors that correlate with the productivity increase. Principal component analysis (PCA) is a technique to extract key factors from a larger set of variables by analyzing variance–covariance differences among a set of variables. The extracted factors preserve large proportions of the original set of variables while reducing the number of variables in order to support an understanding of the structure of latent extracted factors [31].

Principal component analysis was conducted for variables BMC1 to BMC8 with the direct oblimin rotation method since we encountered correlated variables during the correlation analysis (Table 5). The suitability of the PCA was tested prior to analysis. An inspection of the correlation matrix showed that all analyzed variables had at least one correlation coefficient higher than 0.3. The overall Kaiser–Meyer–Olkin (KMO) measure was 0.869 and Bartlett’s test of sphericity was statistically significant, chi-square (28) = 485, p < 0.001, which confirmed that the sample data were appropriate for PCA analysis.

The number of extracted factors was determined based on Kaiser’s criteria of an eigenvalue > 1 (

Table 6. Initial eigenvalues and total variance explained.

Component	Initial Eigenvalues			Rotation Sums of Squared Loadings 1
	Total	% of Variance	Cumulative %	Total
1	5.047	63.086	63.086	4.634
2	1.233	15.408	78.494	3.595
3	0.507	6.337	84.831
4	0.410	5.128	89.959
5	0.268	3.346	93.305
6	0.200	2.505	95.809
7	0.190	2.375	98.184
8	0.145	1.816	100.000

Extraction method: principal component analysis. ¹ When components are correlated, the sums of squared loadings cannot be added to obtain the total variance.

) and the Cattel scree plot (Figure 2).

PCA revealed two components that have eigenvalues greater than one and which explained 63.08% and 15.41% of the total variance (Table 6).

The eigenvalue-one criterion is a method for establishing how many components to retain in a principal components analysis. An eigenvalue less than one indicates that the component explains less variance that a variable would and hence should not be retained. The visual inspection of the scree plot (Figure 2) also indicated that the research should proceed with two-component solution [32,33]. In addition, a two-component solution met the interpretability criterion since the research aimed to extract factors from the variables set in order to explain which digital transformation business model changes factors and how they impact the productivity increase.

The two-component solution explained 78.49% of the total variance. Component loadings of the rotated pattern matrix solution are presented in

Table 7. Principal component analysis pattern matrix rotated solution.

		Component
		1	2
BMC5	The organization has entered new markets as a result of digitalization and digital transformation	0.931
BMC4	The organization generates additional revenue from new sources as a result of the implementation of digital technologies	0.927
BMC7	The organization has introduced new sales channels as a result of digitalization and digital transformation	0.910
BMC6	The organization provides new services as a result of digitalization and digital transformation	0.860
BMC8	The organization has introduced new ways of charging for services as a result of digitalization and digital transformation	0.673
BMC2	The organization has digitalized internal business processes		0.982
BMC3	The organization has digitalized external business processes		0.901
BMC1	The organization cooperates with new partners with the aim of developing new digital solutions		0.689

Extraction method: principal component analysis. Rotation method: oblimin with Kaiser normalization ¹. ¹ Rotation converged in four iterations.

. To contrast the solution, we set a cutoff value of 0.50 in order to have single variable representing each principal component [31].

The rotated solution presents a simple structure of the two main factors. The interpretation of the extracted two-factor solution is consistent with the digitalization and innovation attributes in the questionnaire, and both exhibit strong loadings (>0.90). Based on the pattern-matrix-rotated solution, latent variables for component 1 as innovation and process digitalization for component 2 were defined.

As a result of PCA analysis, regression factor scores were calculated for each sample case for both solution factors, which were then taken for a further correlation analysis to confirm the correlation between two new component solution factors (innovation and process digitalization) and the productivity increase. The results of this final correlation analysis are presented in

Table 8. Correlations.

			PROD	REGR Factor Score 1	REGR Factor Score 2
Spearman’s rho	The organization has increased productivity by introducing digital transformation (PROD)	Correlation coefficient
		Sig. (2-tailed)
		N	82
	REGR factor score 1 (innovation)	Correlation coefficient	0.314 **
		Sig. (2-tailed)	0.004
		N	82	82
	REGR factor score 2 (process digitalization)	Correlation coefficient	0.443 **	0.453 **
		Sig. (2-tailed)	<0.001	<0.001
		N	82	82	82

** Correlation is significant at the 0.01 level (2-tailed).

. Since the PROD variable was of the ordinal type, Spearman rho correlation analysis was used. Preliminary analysis showed the relationship to be monotonic, as assessed by a visual inspection of the scatterplot.

The Spearman’s rank-order correlation analysis results confirmed the statistically significant correlation between innovation and the productivity increase, rs = 0.314, p = 0.004, and between process digitalization and the productivity increase rs = 0.443, p < 0.001.

4. Discussion

The transformation of the maritime industry through digital technologies is no longer a future ambition but a present necessity, shaped by the rapid evolution of logistics networks, stakeholder expectations, and global economic pressures. The empirical results of this study add a valuable dimension to our understanding of how digital transformation—when implemented through business model changes—can serve as a mechanism for increase of productivity in maritime sector organizations.

The results confirm the statistically significant correlation between digital transformation business model changes and an increase in productivity. The study identified two key factors that mediate the relationship between digital transformation and productivity in maritime organizations: innovation and Process digitalization. The factors were extracted through the principal component analysis of the eight variables related to the digital transformation business model changes. The correlation analysis also confirmed the significant relationship between these two factors and the increase in productivity. The findings of the study correspond with existing recent literature, which suggests that digital transformation can lead to improved operational efficiency and customer engagement, thereby increasing productivity [1].

The innovation factor, which encompasses the organization’s ability to generate additional revenue from new sources, enter new markets, introduce new sales channels, and provide new services as a result of digital transformation, is confirmed to be significantly correlated with a productivity increase. This suggests that maritime organizations that innovate in their digital transformation are likely to experience a significant increase in productivity. This finding is in line with the broader literature on digital transformation, which suggests that innovation is a critical component of successful digital transformation efforts [2].

The process digitalization factor, which includes the digitalization of internal and external business processes and collaboration with new partners to develop new digital solutions, also correlates significantly with increased productivity. This indicates that maritime organizations that effectively digitalize their business processes can achieve a higher level of productivity. This indication is also consistent with the existing literature, which indicates that the digitalization of processes can lead to improved operational efficiency and productivity [2].

A key insight emerging from this research is the multidimensional nature of the transformation itself. Rather than being an isolated or uniform change, digital transformation manifests itself across a spectrum of activities, ranging from shifts in organizational behavior and external partnerships to the reimagining of revenue logic. This complexity supports the view expressed by [4,5] that transformation is not simply a question of adopting new technologies, but also involves a reevaluation of organizational logic, structures, and interactions with external ecosystems.

In the context of the maritime sector—often portrayed as conservative and resistant to rapid change—the identification of several statistically meaningful correlations between transformation variables and productivity provides a promising outlook. These correlations underscore the tangible effects that properly directed digital initiatives can have, even within highly regulated and operationally intensive industries such as port operations, shipping, and multimodal logistics.

The broader literature echoes the importance of this finding. For instance, successful innovation adoption in ports is frequently contingent on a combination of the internal capacity (such as management support and resource availability) and external enablers (like stakeholder alignment and policy frameworks) [13]. The alignment of such multidirectional efforts is reflected in this study’s findings, particularly in the interconnectedness of variables across internal operations, the service design, and market interactions.

Furthermore, the methodological framework adopted in this research—leveraging the TOE model—proves effective in dissecting the drivers behind transformation. The technological, organizational, and environmental categories outlined in the TOE framework reveal how interdependent elements shape transformation outcomes. For example, the presence of technological infrastructure without accompanying organizational readiness, or vice versa, may limit the effectiveness of any digital strategy. These interrelations are supported by the researchers who found that digital maturity in shipping firms was not only a matter of infrastructure but also a function of managerial commitment and institutional adaptability [1].

One important area for interpretation lies in the evolution of service delivery in the maritime transport domain. The literature further demonstrates that digitalization efforts—such as mobile ticketing systems, chatbot deployment, or digital freight platforms—represent more than incremental process enhancements; they constitute foundational shifts in how value is delivered to customers and partners [11]. When mapped to the data in this study, these examples highlight the strategic dimension of transformation, where innovation is deployed not merely for efficiency but also for differentiation and competitiveness.

Additionally, the significance of networked innovation—wherein organizations collaborate across traditional boundaries to co-develop or co-deliver value—cannot be overstated. The data analyzed here suggest that inter-organizational cooperation plays a critical role in facilitating access to new markets, developing tailored digital solutions, and unlocking synergies in logistics chains. This is particularly relevant in maritime clusters, where port authorities, carriers, customs agencies, and third-party logistics providers must operate in coordination to achieve true digital cohesion. Prior research supports this notion by illustrating how the smart port concept relies on such collaborations to achieve shared efficiencies and responsiveness [16,18].

From a theoretical standpoint, the results may also be viewed through the lens of the Dynamic Capabilities Theory, which posits that an organization’s ability to sense, seize, and reconfigure resources in a rapidly changing environment determines its competitive position. In this context, digital transformation is a means of enhancing dynamic capabilities, especially in relation to strategic alignment, resource orchestration, and customer responsiveness.

Interestingly, while the study employed a quantitative approach, the patterns it reveals point toward deeper organizational dynamics that might be more fully unpacked through qualitative methodologies. For instance, what motivates certain organizations to adopt more aggressive transformation strategies than others? What internal cultural or leadership factors facilitate or hinder this process? These are questions best addressed through longitudinal case studies or ethnographic inquiry, offering a richer understanding of the change process.

Another avenue for reflection is the role of resilience in the digital strategy. As the maritime industry faces mounting pressures from climate change, global disruptions (e.g., pandemics), and cybersecurity threats, digital transformation must also be framed as a resilience-building activity. The literature suggests that digital orchestration mechanisms during crises can help firms navigate uncertainty more effectively [20]. This view encourages future research to explore how transformation not only boosts productivity in stable conditions but also fortifies organizations against volatility.

5. Conclusions

The findings of this study underscore the significance of business model changes, particularly innovation and process digitalization, as key factors of productivity enhancement in Croatian maritime transport stakeholders undergoing digital transformation. The BMC construct consists of eight distinct questions because digital transformation in maritime organizations is multifaceted, involving numerous interrelated changes in business models. Digital transformation is not a singular technological shift but involves comprehensive innovation and process alterations that impact various organizational aspects. The construct thus includes questions covering key dimensions, such as cooperation with partners for digital solutions (BMC1), internal (BMC2) and external (BMC3) business process digitalization, the creation of new revenue streams (BMC4), market entry through digital transformation (BMC5), the provision of new services (BMC6), the introduction of new sales channels (BMC7), and the implementation of novel pricing models (BMC8). Each of these questions represents a specific business model innovation or operational area directly impacted by digital transformation. The thoroughness of this construct, validated by a high Cronbach’s alpha (0.904), demonstrates its relevance and internal consistency, capturing the comprehensive effects digital transformation has on business models within maritime transport stakeholders.

The strong positive correlations identified between productivity and specific transformation activities—such as internal and external process digitalization (BMC2, BMC3), revenue generation from new sources (BMC4), and the introduction of new services and pricing models (BMC6, BMC8)—reaffirm the multifaceted benefits of digital innovation across operational and strategic levels.

These results align with the broader literature emphasizing that digital transformation, when coupled with business model innovation, leads to superior organizational performance and efficiency. The identification of two principal components is consistent with previous studies that suggest that digital business model innovation involves both the reconfiguration of value creation mechanisms and the implementation of enabling technologies. These two main components—innovation (e.g., new services, markets, revenue streams) and process digitalization (e.g., internal/external process automation, digital partnerships)—were identified through principal component analysis as key mediators of this relationship. The research demonstrates strong statistical correlations between these components and productivity gains, highlighting that digital transformation must go beyond technology adoption to include the strategic rethinking of value delivery. Compared to Western European ports, Croatia is still in the early stages of digital maturity, facing challenges such as fragmented systems and conservative organizational cultures. However, there is growing momentum, driven by EU initiatives and national policies, to modernize operations and better integrate into the European digital maritime logistics ecosystem.

Specifically, the positive relationship between process digitalization and productivity supports the growing body of evidence that digital technologies such as IoT, AI, and integrated ERP systems streamline operations, reduce errors, and enhance coordination. Likewise, the innovation component—characterized by new services, markets, and revenue streams—mirrors trends in digitally mature organizations that pursue service diversification and customer-centric strategies to stay competitive.

Importantly, the strong statistical significance found in correlations suggests that digital transformation cannot be effective in isolation; it must be accompanied by strategic rethinking of how value is delivered and captured. This is especially relevant in the maritime sector, where traditional practices often hinder agility and integration. The results thus echo calls for a holistic transformation framework that combines technological readiness, organizational capability, and a conducive external environment—core pillars of the TOE framework used in this study.

In practice, this means that maritime transport stakeholders should prioritize investments not just in digital tools but also in organizational change management, workforce upskilling, and collaborative innovation with external partners. As digital ecosystems become more interconnected, fostering interoperability and secure data exchange becomes crucial for realizing productivity gains across the value chain.

While this study provides valuable insights into the relationship among digital transformation, business model innovation, and productivity in maritime transport stakeholders, several limitations should be acknowledged. First, the research is based on a cross-sectional survey design, which captures data at a single point in time and limits the ability to establish causality between digital transformation efforts and productivity outcomes. Longitudinal studies would be more suitable for tracking the dynamic and evolving nature of digital transformation. Second, the sample is geographically restricted to a single country, Croatia, which, although strategically significant in European maritime logistics, may limit the generalizability of findings to other regions with different regulatory, technological, and economic environments. Furthermore, the sample size is fairly limited, although it is of an appropriate size for the statistical methods used. On the other hand, it has limitations as it includes organizations of different sizes. To overcome this issue the sample size could be larger to include more medium and large organizations, or it could be revenue- or turnover-weighted. Third, the reliance on self-reported data may introduce subjective bias, particularly in responses concerning productivity improvements and strategic outcomes. Additionally, the study uses a single-item measure for productivity, which, although supported by the internal consistency of related constructs, may not capture the full complexity of performance metrics in maritime transport stakeholders and may limit the robustness of the findings. The Technology–Organization–Environment (TOE) framework was particularly relevant to this study because it offers a structured approach for examining the complexity of digital transformation initiatives. We have employed the TOE framework introduced by Tornatzky and Fleischer precisely due to its ability to effectively analyze three critical dimensions: technological adoption, organizational preparedness, and environmental influences. In the maritime sector, these dimensions play crucial roles, as digital transformation often faces technological barriers (e.g., legacy systems, cybersecurity), organizational resistance (e.g., cultural inertia, lack of management support), and environmental complexities (e.g., regulatory compliance, stakeholder coordination). By utilizing the TOE framework, influencing factors have been systematically identified, categorized, and analyzed. They mediate the relationship between digital transformation-induced business model changes and productivity outcomes. This structured analysis thus makes the TOE framework especially suitable for the comprehensive evaluation of digital transformation’s multidimensional impact on maritime transport organizations, providing clarity and coherence to the empirical findings of the research. However, while the TOE framework provides a structured approach to analyzing digital transformation, it may not fully account for the nuanced, industry-specific drivers of innovation in maritime contexts, such as geopolitical factors or evolving sustainability regulations.

For port authorities and maritime firms, the findings suggest that investing in digital transformation should prioritize not only technology upgrades but also organizational innovation and collaboration. Implementing internal and external process digitalization—such as automating documentation, adopting real-time tracking, or integrating ERP systems—can significantly boost efficiency. Moreover, developing new digital services, revenue models, and partnerships enables firms to remain competitive and responsive to changing logistics demands. These strategic shifts are essential for aligning with EU digitalization goals and improving integration into trans-European transport networks.

Future research could involve expanding the scope of productivity metrics beyond self-reported perceptions to include objective performance indicators (e.g., cargo throughput, turnaround time, cost savings), which would enhance the robustness of future analyses. Qualitative methods such as case studies or interviews could complement the survey findings by capturing the lived experiences of stakeholders and providing a deeper understanding of organizational change dynamics. Additionally, integrating advanced analytical methods—such as structural equation modeling (SEM) or machine learning—could offer more precise models of causality and prediction. Finally, future studies might examine the role of digital skills, leadership, and cross-sector collaboration as mediators or moderators in the relationship between digital transformation and business outcomes, further enriching the theoretical and practical contributions of this research stream. As digital transformation advances, maritime organizations must address the growing complexity of securing digital infrastructures and navigating inconsistent regulatory frameworks across jurisdictions. A more thorough analysis of these risks would provide valuable guidance for mitigating barriers to innovation and ensuring resilient, compliant operations.

Overall, the study contributes to a growing understanding of how targeted digital strategies can yield tangible productivity benefits in the maritime sector. It also provides a structured empirical foundation for policy-makers and industry leaders aiming to accelerate digital maturity and competitive positioning in a sector that is increasingly under pressure to innovate sustainably.