Introduction

The maritime and logistics industry, as a crucial pillar of global supply chain management and international trade, is facing unprecedented challenges (Rodrigue and Notteboom, 2020; Tsunoda and Zennyo, 2021). As globalization deepens, shipping networks are becoming increasingly complex, market demand is exhibiting heightened volatility, and countries are continuously upgrading trade regulations and environmental protection policies, making traditional logistics management models inadequate to meet the modern logistics system’s demand for efficiency, transparency, and sustainable operations (Yang and Lin, 2024). Within this framework, digital logistics platforms (DLP) have become fundamental drivers of digital transformation in the maritime and logistics sector. They enable the seamless incorporation of advanced technologies, including cloud computing, big data, blockchain, and the Internet of Things (IoT), to enhance supply chain transparency, intelligence, and cooperative optimization (Xiaoyong et al. 2008). However, despite the immense potential of DLP, their adoption in the maritime and logistics industry faces significant barriers, such as technological integration challenges and low user acceptance (Hadizadeh et al. 2024). Therefore, investigating the key factors influencing DLP adoption intentions is not only of theoretical significance but also offers practical guidance for business decision-making and policy formulation.

Recent studies have explored the application value of DLP and its impact on industry performance from various perspectives. For instance, Emre et al. (2024) conducted a survey among Turkish university students to analyze digital logistics awareness and found significant differences in digital logistics awareness based on educational background but did not delve into industry enterprises. Hadizadeh et al. (2024) investigated how digital platforms contribute to the sustainability of supply chain management. Mendoza Pardo and Fikar (2024) focused on DLP in food supply chains. Yang & Lin (2024) conducted an empirical study revealing the positive impact of DLP applications on business performance in Taiwanese maritime logistics enterprises. Furthermore, Barykin et al. (2021) studied how BRICS countries optimize digital supply chains by building autonomous trade logistics platforms and proposing strategies to leverage DLP for global trade connectivity. However, existing research primarily focuses on manufacturing, food supply chains, or regional logistics. It lacks an in-depth investigation into the specific factors influencing DLP adoption in the maritime logistics industry, the technology acceptance model of enterprises, and the systemic impact (Yang and Lin, 2024). The maritime and logistics sector encompasses various stakeholders, such as shipping firms, port administrators, freight brokers, and customs authorities, which adds complexity to the process of adopting new technologies due to intricate market dynamics and regulatory frameworks. Therefore, further research is needed to explore the DLP adoption pathway in the maritime logistics sector and clarify the industry’s unique technological diffusion patterns and key decision-making factors.

This study is grounded in the stimulus–organism–response (SOR) framework, integrating the diffusion of innovations theory and the extended technology acceptance model (ETAM) to develop a novel second-order SEM. The aim is to systematically examine the impact of diffusion of innovations, technological perception factors, and flow experience characteristics on the adoption intention of digital logistics platforms in maritime logistics enterprises. While the SOR framework primarily focuses on individual or organizational behavioral decisions in specific contexts, it does not directly explain the dynamic process of technology diffusion. This study complements the SOR framework with the diffusion of innovations theory (IDT) and ETAM. First, the study treats diffusion of innovation factors (relative advantage, compatibility, complexity) as the stimulus variables (S) within the SOR framework, integrating the environmental-psychological-behavioral path of SOR while considering the dynamic characteristics of innovation diffusion to explore the stimulus factors driving DLP adoption in the maritime logistics industry. Second, the technology acceptance model (TAM) focuses on individual user perceptions and rational decision-making processes (Lingling and Ye, 2023). This study introduces flow experience to extend the TAM model, addressing ETAM’s limitations regarding user immersion. The SOR framework elucidates the impact of external environmental factors on users’ psychological states while also highlighting the role of emotions in shaping behavioral outcomes (Zhu et al. 2020). This research integrates the SOR framework with ETAM to complement ETAM’s limitations in studying environmental impacts.

This research offers multiple significant contributions. First, it expands the technology acceptance model by introducing the flow experience dimension, responding to the growing attention in the maritime logistics industry toward optimizing user experience in digital technology applications. Second, for the first time, a second-order SEM is used to construct a novel theoretical framework that systematically differentiates and compares the direct and indirect effects of technological perception factors and flow experience on the adoption process of digital logistics platforms, revealing significant differences in their roles within user decision-making, enriching the theoretical perspectives in the fields of technological perception and flow experience. Furthermore, this study delves into the complex mechanism through which external stimuli (diffusion of innovations) and individual factors (technological perception and flow experience) jointly impact the adoption intention, offering a comprehensive understanding of their synergistic effects. Finally, this research identifies key pathways for increasing DLP adoption rates, providing systematic theoretical support and practical guidance for market promotion, user experience optimization, and policy formulation of digital logistics platforms.

This study is structured as follows: Section 2 provides a comprehensive review of relevant literature, serving as the foundation for the theoretical framework and empirical investigation that follow. Section 3 presents the data sources, research strategies, and overall analytical approach. Section 4 presents and explains the research results and provides further discussion of key issues. Section 5 discusses the theoretical significance and practical value of the findings. Finally, Section 6 concludes the paper, summarizing the key conclusions, addressing the limitations of the study in terms of scope and methodology, and suggesting directions for future research.

Literature review and research hypotheses

Digital logistics platform (DLP)

The maritime and logistics industry is a cornerstone of global trade, playing a crucial role in optimizing resource allocation, enhancing supply chain resilience, and driving economic integration (Yang and Lin, 2024). However, as globalization accelerates, the industry faces unprecedented challenges, including increasingly complex international shipping networks, stringent regulatory requirements, market demand volatility, and mounting environmental sustainability pressures (Paksoy et al. 2021). Traditional logistics management, reliant on manual operations and fragmented information flows, suffers from inefficiencies such as information asymmetry, delayed decision-making, and low operational efficiency, making it inadequate for the industry’s growing demand for precision, efficiency, and sustainability (Rodrigue and Notteboom, 2020). Against this backdrop, digital transformation has become a strategic imperative for improving operational efficiency, optimizing supply chain management, and enhancing corporate competitiveness (Saeedikiya et al. 2025). As a key enabler of this transformation, DLP integrates advanced information technologies with logistics management, enabling end-to-end visibility, intelligent optimization, and collaborative decision-making, thereby accelerating the industry’s transition toward smart and sustainable logistics (Kayikci, 2018; Xiaoyong et al. 2008). The core value of DLP lies in increasing transparency, automation, and intelligent decision-making in logistics operations, with primary functionalities including data-driven supply chain optimization (Yang and Lin, 2024), enhanced visibility and transparency (Winkelhaus and Grosse, 2020), intelligent scheduling and automated operations (Tsunoda and Zennyo, 2021), and cross-border trade standardization (Rodrigue and Notteboom, 2020).

In recent years, scholars have explored DLP applications and their impacts from diverse perspectives. Emre et al. (2024) examined digital logistics awareness among Turkish university students, revealing that educational background influences digital logistics cognition, underscoring the importance of talent development in the industry. Hadizadeh et al. (2024) investigated the role of digital platforms in promoting sustainability, highlighting their ability to optimize resource utilization through data processing and information sharing, thereby facilitating sustainable supply chain transformation. Mendoza Pardo and Fikar (2024) analyzed the application of DLP in food supply chains and found that they effectively reduce communication barriers and enhance supply chain efficiency. Yang and Lin (2024) empirically demonstrated that DLP adoption significantly improves the performance of Taiwanese maritime logistics enterprises, particularly in terms of operational efficiency and customer satisfaction. Additionally, Barykin et al. (2021) explored how BRICS nations leverage autonomous trade logistics platforms to optimize digital supply chains, proposing strategic pathways for enhancing global trade connectivity through DLP. Collectively, these studies underscore the extensive application value of DLP across industries, demonstrating their ability to improve responsiveness and drive innovation in supply chain management.

Despite these contributions, several research gaps remain. First, existing studies lack a systematic analysis of the DLP adoption mechanisms within the maritime and logistics industry. Most research has focused on manufacturing, food supply chains, or regional logistics, with limited exploration of the adoption process, influencing factors, and technology acceptance mechanisms specific to maritime logistics (Yang and Lin, 2024). Second, prior studies have largely overlooked the interaction between technology diffusion and user experience factors. While most research emphasizes DLP technical attributes, such as data integration and intelligent optimization, the interplay between diffusion of innovations, technological perception, and user emotional engagement in the adoption process remains underexplored. In particular, flow experience, a crucial factor influencing technology adoption, has not been thoroughly explored within the context of DLP adoption (Kayikci, 2018). Third, existing research predominantly employs qualitative methodologies, such as case studies and surveys, without systematically modeling the complex decision-making process and multidimensional interactions affecting DLP adoption, as illustrated in Table 1. To address these gaps, this study integrates the SOR framework with the IDT and the ETAM to develop a second-order SEM. This framework comprehensively investigates how the diffusion of innovations, technological perceptions, and flow experience interact to influence the adoption of digital logistics platforms within the maritime and logistics sector.

Table 1 Research model literature abstracts.
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Theoretical framework

This study integrates the SOR framework, the IDT, and the ETAM to develop a comprehensive second-order SEM. This model aims to elucidate the intricate mechanisms by which external stimuli (diffusion of innovations) and individual factors (technological perception and flow experience) shape intention to use, as illustrated in Fig. 1.

Fig. 1
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The theoretical framework of DLP and adoption intentions.

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SOR framework

The SOR framework, originally proposed by Mehrabian and Russell (1974), explains how external environments influence individuals’ emotional and cognitive states (organism, O), thereby shaping behavioral responses (response, R). This framework posits that external stimuli (S) affect an individual’s psychological and emotional state (O), ultimately leading to behavioral changes (R) (Eroglu et al. 2003). As digital technologies become more prevalent in the logistics sector, the SOR framework has evolved beyond its conventional focus on consumer behavior, now extending to domains like supply chain management, technology adoption, and digital transformation (Zhu et al. 2020; Liu et al. 2023). In the maritime and logistics industry, the SOR framework has been widely applied to studies on enterprise technology adoption (Duong and Nguyen, 2024), digital transformation in logistics firms (Paksoy et al. 2021), user experience in logistics technology and supply chain optimization (Lingling and Ye, 2023), and technology adoption decisions in maritime logistics (Yang et al. 2022). Within this framework, “stimuli” refer to various internal and external factors in the environment that influence an individual’s perceptions and behaviors. The “organism” represents an individual’s psychological and emotional responses, which, to a certain extent, determine how they react to external stimuli (Guo et al. 2022). Within the maritime and logistics sector, organism variables generally include employees’ emotional responses, attitudes, technological perceptions, and willingness to adopt innovations. For example, in the process of digital transformation, employees’ willingness to adopt new technologies plays a crucial role in determining the effectiveness of technology implementation (Saeedikiya et al. 2025). The “response” denotes the behavioral reactions that individuals exhibit when exposed to external stimuli and their internal psychological state (Yang et al. 2022).

In summary, the SOR framework provides a solid theoretical foundation for understanding the psychological mechanisms behind DLP adoption in the maritime and logistics industry. While the SOR framework primarily focuses on individual or organizational decision-making in specific contexts, it does not directly explain the dynamic process of technology diffusion. To address this limitation, this study integrates the IDT and the ETAM. Specifically, innovation diffusion factors such as relative advantage, compatibility, and complexity are incorporated as stimulus variables (S) within the SOR framework. By integrating the SOR framework’s environment–psychology–behavior path with the dynamic characteristics of diffusion of innovations, this study explores the key drivers of DLP adoption in the maritime and logistics industry.

Innovation diffusion theory (IDT)

The IDT, proposed by Rogers et al. (2014), explains how new technologies spread within a social system and influence individual or organizational adoption decisions. This theory posits that the diffusion of innovation is shaped by multiple factors, including relative advantage, compatibility, complexity, trialability, and observability (Rogers, 2003). These factors collectively determine the extent and speed of adoption among individuals or organizations (Rogers et al. 2014; Sahin, 2006). In recent years, IDT has been extensively utilized in logistics and supply chain management, covering areas such as the adoption of smart logistics and automation technologies (Paksoy et al. 2021; Huo et al. 2023), the influence of digital transformation on organizational performance (Yang and Lin, 2024), the evolution of logistics platform ecosystems (Barykin et al. 2021), and the dissemination of data-sharing and supply chain visibility solutions (Hadizadeh et al. 2024).

Within the context of DLP, IDT effectively explains how enterprises adopt emerging logistics technologies. For instance, DLP enhances logistics management efficiency, improves data transparency, and optimizes supply chain collaboration (Xiaoyong et al. 2008). However, the adoption of DLP varies across logistics enterprises due to factors such as technological compatibility, industry standardization, data security, and market demand (Winkelhaus and Grosse, 2020). Although IDT plays a pivotal role in explaining technology diffusion, it primarily focuses on technological attributes such as relative advantage and compatibility, without delving deeply into user perceptions and experiences (Zhu et al. 2020; Liu et al. 2023). To overcome this limitation, this research combines the SOR framework with IDT to conduct a systematic analysis of the factors influencing DLP adoption intentions within the maritime and logistics sector.

Extended TAM (ETAM)

The TAM, proposed by Davis (1989), explains how individuals adopt and use new technologies. Based on the TAM, technology adoption behavior is primarily influenced by perceived usefulness (PU) and perceived ease of use (PEOU). Perceived usefulness denotes an individual’s belief that technology improves work efficiency (Davis, 1989), whereas perceived ease of use represents the degree to which a technology is regarded as simple to operate. However, the traditional TAM primarily focuses on rational cognitive variables while overlooking users’ emotional experiences during technology use, limiting its applicability in explaining complex technology adoption behaviors. To address this, scholars have proposed the ETAM by incorporating factors such as user emotions, social influence, and usage motivation to enhance its explanatory power (Venkatesh et al. 2012). ETAM has been widely applied in studies on the adoption of emerging logistics technologies, including logistics automation acceptance (Winkelhaus and Grosse, 2020; Huo et al. 2023), supply chain visualization and intelligent technology adoption (Guo et al. 2016), and user experience optimization in digital platforms (Yang and Lin, 2024).

Csikszentmihalyi and Csikzentmihaly (1990) introduced the concept of flow experience, which characterizes a psychological state where individuals are deeply engaged in an activity, maintaining intense focus while deriving a sense of enjoyment. Its characteristics include deep concentration, immediate feedback, and altered time perception. In technology adoption research, flow experience is recognized as a critical emotional driver that enhances users’ acceptance and continuous use of technology (Guo et al. 2016). While traditional TAM primarily emphasizes rational cognitive variables such as perceived usefulness and perceived ease of use, user experience and emotional factors are equally vital in the adoption of DLP. Particularly in the logistics industry, DLP adoption not only relies on technical advantages but also requires enhanced user interaction and immersion to reduce resistance and improve adoption rates. Thus, this research integrates flow experience within the ETAM framework to offer a more holistic perspective on the determinants of DLP adoption.

TAM primarily focuses on individual cognition, perception, and rational decision-making (Lingling and Ye, 2023), whereas the SOR framework explains how external environmental stimuli influence psychological states and emphasizes the role of emotions in behavioral responses (Zhu et al. 2020). To address ETAM’s limitations in analyzing environmental influences and user immersion, this study integrates flow experience into the TAM model and combines the SOR framework with ETAM, offering a more holistic perspective on DLP adoption in the maritime and logistics industry.

Models and assumptions

Innovation diffusion and technology adoption in DLP

Diffusion of Innovations refers to the process by which new technologies are adopted and spread within organizations or markets (Rogers et al. 2014). In the maritime and logistics industry, the adoption of DLP as an emerging digital management tool is influenced by multiple factors, including technological attributes, market conditions, and user perceptions (Haddad and Nasib, 2023). Key elements of the diffusion of innovations include compatibility, relative advantage, complexity, and observability, all of which directly impact users’ technological perception (Parthasarathy et al. 2019). Relative advantage reflects the degree of improvement a DLP offers over traditional logistics management methods (Yang and Lin 2024). Existing research suggests that when users perceive a new technology as significantly enhancing operational efficiency, their perceived usefulness increases accordingly (Crespo et al. 2013). In the maritime and logistics industry, DLP enhances efficiency by integrating data, optimizing scheduling, and enabling real-time monitoring, thereby reducing transportation delays, lowering inventory costs, and improving overall operational performance (Wang et al. 2018). Compatibility refers to how seamlessly a new technology integrates into existing systems or workflows (Rogers et al. 2014). Since maritime logistics companies operate various management systems, including port management systems (PMS), transportation management systems (TMS), and warehouse management systems (WMS), DLP must possess strong compatibility to facilitate smooth integration (Orser and Riding, 2018). Studies indicate that higher technological compatibility strengthens users’ perceived usefulness (Kıymalıoğlu et al. 2024). When DLP can be effortlessly incorporated into existing logistics management frameworks, enterprises are more likely to recognize their practical value. Complexity denotes the difficulty users face in learning and utilizing a new technology. Research has shown that excessive complexity increases cognitive load, reducing users’ perceived usefulness (Tandon et al. 2016). In the maritime and logistics industry, if DLP has a complicated interface, requires extensive training, or entails high integration costs, enterprises may perceive them as challenging to implement, thereby lowering adoption willingness (Parthasarathy et al. 2019). Based on the above analysis, this study proposes the following hypotheses:

H1: DLP’s innovation diffusion positively affects perceived usefulness.

H2: DLP’s innovation diffusion positively affects perceived ease of use.

The TAM suggests that users’ decisions to adopt technology are primarily influenced by perceived usefulness and perceived ease of use (Davis, 1989). When users recognize technology as both improving work efficiency (PU) and being user-friendly (PEOU), their willingness to adopt it grows substantially (Venkatesh and Bala, 2008). In the maritime and logistics industry, DLP serves as a crucial enabler of digital transformation. Their adoption is influenced by factors such as compatibility, complexity, relative advantage, and user experience (Yang and Lin 2024). According to the Innovation Diffusion Theory (IDT), factors such as compatibility, relative advantage, and complexity influence users’ perceptions of usefulness and ease of use, which in turn impact their technology adoption choices (Rogers et al. 2014). Therefore, increasing users’ perception of DLP’s usefulness and ease of use is essential to enhancing adoption rates. Perceived usefulness reflects users’ beliefs regarding DLP’s ability to improve work efficiency, reduce operational costs, and optimize supply chain management (Davis, 1989). Research indicates that when enterprises perceive DLP as providing significant operational advantages and enhancing logistics management capabilities, their adoption intention increases substantially (Crespo et al. 2013). Perceived ease of use reflects users’ assessment of how simple DLP is to learn and operate, while also reducing implementation difficulties and operational expenses (Davis, 1989). Studies have shown that when users find a new technology intuitive and user-friendly, they are more likely to adopt it (Venkatesh and Bala, 2008). Building on this analysis, the following hypotheses are proposed:

H1-1: Perceived usefulness mediates the relationship between the diffusion of innovations and the intention to use DLP.

H2-1: Perceived ease of use mediates the relationship between diffusion of innovations and intention to use DLP.

Experience refers to the psychological state and emotional perception users develop when engaging with a platform or technology, playing a crucial role in shaping user behavior (Ha et al. 2007). In the context of digital transformation in the maritime and logistics industry, DLP must not only meet enterprise needs in terms of compatibility, relative advantage, and ease of use but also deliver a seamless user experience to enhance immersion and intention to use (Lu and Hsiao, 2022). Flow experience, a deeply immersive psychological state, reflects the degree of focus, enjoyment, and self-efficacy users experience while interacting with technology (Csikszentmihalyi et al. 2014). Studies indicate that flow experience significantly influences technology acceptance, user satisfaction, and continued usage intention (Wang and Wang, 2020; Park et al. 2023). In the DLP adoption process, the diffusion of innovations can enhance user intention to use by improving immersion and interactive experiences (Sánchez-Fernández and Iniesta-Bonillo, 2007; Turel et al. 2010). According to the IDT, key technological attributes such as relative advantage, compatibility, and observability enhance perceived value and trust in technology, ultimately strengthening flow experience (Rogers et al. 2014). In the maritime and logistics industry, the diffusion of DLP can optimize technological features and user interactions, fostering a heightened sense of immersion and improving the flow experience (Lu and Hsiao, 2022). Research has shown that flow experience enhances user satisfaction and intention to use while mediating the relationship between the diffusion of innovations and ultimate technology adoption (Sánchez-Fernández and Iniesta-Bonillo, 2007). During DLP usage, users who experience a higher flow experience are more likely to recognize the technology’s value and commit to long-term adoption (Ha et al. 2007; Park et al. 2023). Building on this analysis, the following hypotheses are proposed:

H3: DLP’s innovation diffusion positively affects the flow experience.

H3-1: Flow experience mediates the relationship between the diffusion of innovations and the intention to use DLP.

Technology acceptance and intention to use

The TAM has been extensively utilized in different fields to explore the impact of perceived usefulness and ease of use on users’ technology adoption intentions (Davis, 1989; Venkatesh and Bala, 2008). In the maritime and logistics industry, DLP serves as a key driver of digital transformation, and their adoption rate is significantly influenced by users’ perceptions of their usefulness and ease of use (Silva et al. 2022). Previous studies indicate that perceived ease of use directly impacts users’ intention to adopt technology while also indirectly shaping adoption behavior by increasing perceived usefulness (Lu et al. 2007; Koh et al. 2024). Perceived usefulness is one of the most critical factors in technology adoption, particularly in the maritime and logistics industry, where enterprises assess whether DLP can improve operational efficiency, reduce costs, and optimize supply chain management (Lu et al. 2007). Studies indicate that when enterprises perceive DLP as delivering measurable business value, their intention to use the technology significantly increases (Dabić et al. 2024). Building on this analysis, the following hypotheses are proposed:

H4: Perceived usefulness of DLP has a positive effect on intention to use.

H5: Perceived ease of use of DLP positively influences intention to use.

Flow experience and intention to use

Flow experience refers to a psychological state of deep focus, immersion, and enjoyment that users experience while interacting with a technology or platform (Ghani and Deshpande, 1994). In the context of digital transformation in the maritime and logistics industry, the successful adoption of DLP depends not only on compatibility and ease of use but also on users’ experiential perceptions during platform interaction. Research indicates that flow experience enhances user engagement, increases technology satisfaction, and promotes intention to use new technologies (Arghashi and Yuksel, 2022; Zhao et al. 2020). In the DLP adoption process, when users experience a flow experience, characterized by deep concentration, seamless interaction, and enjoyment while using the platform, they are more likely to adopt it (Park et al. 2023). Flow experience has been widely studied in technology acceptance, consumer behavior, and digital platform research, with findings demonstrating that it significantly strengthens users’ recognition of new technologies and enhances their intention to use them (Csikszentmihalyi et al. 2014). In the maritime and logistics industry, as a digital management tool, DLP’s usability and immersive experience are critical factors influencing enterprise intention to use (Arghashi and Yuksel, 2022). Building on this analysis, the following hypotheses are proposed:

H6: The flow experience of DLP positively influences the intention to use.

Study methodology

This study employs second-order SEM to assess a comprehensive theoretical model that integrates the SOR framework, IDT, and ETAM. Employing second-order SEM allows for the consolidation of multiple correlated first-order latent variables into a single second-order latent variable, simplifying the model for easier understanding and interpretation (Nguyen-Phuoc et al. 2022). Despite its simplified structure, the model maintains a high level of explanatory power, enhancing stability and estimation accuracy (Koufteros et al. 2009). Second-order SEM is particularly suitable for multidimensional constructs, facilitating the analysis of relationships between first-order and second-order factors, thus providing deeper insights into the dynamics of DLP’s innovation diffusion, technology acceptance, and adoption willingness in maritime and logistics organizations.

Design of questionnaire and sampling procedure

The questionnaire for this study was divided into four main sections: participant demographics, DLP innovation diffusion, technology acceptance, and adoption intentions in logistics and maritime organizations. Demographic data included gender, years of experience, organization type, position level, and company size. Innovation diffusion was assessed by examining the compatibility, relative advantage, and complexity of DLP. Technology acceptance metrics included perceived usefulness, perceived ease of use, and flow experience, based on scales adapted from earlier studies (Park and Chen, 2007; Eid, 2009; Yuen et al. 2018, 2020, 2021). Intention to use DLP was measured by users’ intentions for future use, job preference, frequency of use, and openness to new features, as influenced by various studies (Jung et al. 2009; Alfadda and Mahdi, 2021; Matemba and Li, 2018). Additionally, respondents were asked about their interest in expanding DLP use within their organizations.

The survey employed a five-point Likert scale for item evaluation, with response options ranging from 1 (strongly disagree) to 5 (strongly agree). Survey data were collected through the “Sojump” online platform, targeting professionals in the maritime and logistics industry in mainland China. Although the study focuses on mainland China, the sample encompasses diverse regions, work experience levels, and job positions, ensuring high external validity and applicability of the findings (Shafqat et al. 2023). This open-sampling strategy effectively minimizes regional and cultural biases, enhancing the generalizability of the results (Cleveland and Laroche, 2007). The survey was conducted from May 6 to May 18, 2024, using a random sampling method, and participants who completed the questionnaire received compensation via the “Sojump” platform to encourage participation and improve response quality.

To enhance the scale’s applicability, professional translators conducted bidirectional English-Chinese translation and proofreading to ensure semantic accuracy and cultural alignment. The questionnaire was reviewed and revised through multiple iterations to enhance the clarity and validity of its measurement items (Ho, 2024). Prior to the final distribution, a pilot test involving 100 randomly chosen participants was conducted to evaluate its reliability and validity. Based on the feedback, adjustments were made to question wording and structure to reduce misinterpretation bias and enhance measurement precision (Zhao et al. 2024). These refinements ensured that the final questionnaire accurately captured the study variables and hypotheses. Additionally, the median absolute deviation (MAD) outlier detection method was employed to eliminate extreme and missing values (Leys et al. 2013). Out of the 572 questionnaires distributed, 69 responses were identified as outliers and excluded, leaving 503 valid responses, which corresponds to an effective response rate of 87.94%. The final sample size meets the requirements for SEM, ensuring the robustness and explanatory power of the statistical analysis (Kline, 2023).

To control for common method bias (CMB) and ensure data reliability, two approaches were used. First, Harman’s single-factor test was conducted to detect potential biases. The findings revealed that a single factor accounted for 34.814% of the variance, which is considerably lower than the 50% threshold. This confirms that common method bias is not a major concern and validates the measurement tool’s discriminant validity and reliability (Kock, 2015; Jarvis et al. 2003). Second, a single-factor confirmatory factor analysis (CFA) was performed for further validation. The results were χ2/df = 11.139, RMSEA = 0.142, CFI = 0.498, GFI = 0.477, AGFI = 0.411, and SRMR = 0.117, indicating that the data were not significantly affected by method bias, further confirming model validity and reliability (Gefen et al. 2000). Furthermore, skewness and kurtosis coefficients were analyzed to assess the normality of the data distribution. The results indicated that all absolute values remained within the acceptable range of 3 to 8, suggesting that the data closely followed a normal distribution and met the necessary assumptions for SEM analysis (Hair et al. 2010).

Demographic details

The sample for this study was drawn from maritime and logistics industry companies in mainland China, covering multiple regions to ensure geographic diversity and capture regional variations in DLP adoption (Stock Star, 2023; Qianzhan Industry Research Institute, 2024). A nationwide random sampling strategy was employed, with data collected via the “Sojump” platform, resulting in 503 valid responses. Details can be found in Table 2. The respondents included shipping companies, port operators, freight forwarding companies, and logistics service providers, covering multiple sub-sectors within the maritime and logistics industry to enhance sample representativeness (Wong et al. 2008; Li et al. 2022). The sample structure encompassed enterprises of varying sizes, with large enterprises (more than 500 employees) accounting for 76.54% and small and medium-sized enterprises (SMEs, fewer than 500 employees) representing 23.46%. Although SMEs constitute a larger share of the industry, large enterprises play a more significant role in DLP adoption, making them highly representative of this study (Li et al. 2022). Regarding job positions, 62.823% of respondents were general employees, 25.845% were frontline managers, 8.549% were mid-level managers, and 1.789% were senior executives. Given that general employees are the primary users of DLP, their feedback is critical for assessing user experience and technology acceptance (Yang, 2019). Regarding work experience, 41.551% of participants had been in the industry for 10–20 years, while 34.195% had 6–10 years of experience, and 9.344% had fewer than five years. Notably, over 70% had more than six years of industry involvement, ensuring a solid understanding of operations and the ability to provide valuable insights. Industry segmentation showed that the majority of respondents were from shipping companies (37.773%), logistics firms (29.821%), and port operators (25.646%), with these three sectors collectively representing over 93% of the sample. A stratified random sampling approach was used to include respondents from different enterprise sizes, job levels, and market segments, ensuring data representativeness and the generalizability of findings (Stock Star, 2023; Qianzhan Industry Research Institute, 2024).

Table 2 Profile of survey participants.
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Findings and analysis

The data collected were processed using SEM. Initially, a CFA was performed to evaluate the measurement model’s validity and reliability. Subsequently, structural modeling was applied to examine the hypotheses put forth in this research. The analyses were carried out using SPSS 27 and Amos 26 software.

Validation factor analysis

In this study, diffusion of innovations is conceptualized as a second-order latent variable, encompassing the shared characteristics of three first-order factors: compatibility, relative advantage, and complexity. According to the reflective indicator theory, a second-order factor serves as a conceptual abstraction of its first-order dimensions, driving their performance as a dependent variable. Meanwhile, first-order factors are reflected by their respective measurement variables and exhibit high intercorrelations (Bagozzi and Yi, 2012). This modeling assumption aligns with the fundamental principles of factor analysis, which suggest that correlations among first-order factors originate from an underlying higher-order latent construct, sharing the common attributes of a second-order latent variable (Diamantopoulos and Winklhofer, 2001). Specifically, compatibility, relative advantage, and complexity represent distinct manifestations of the diffusion of innovations, and their significant intercorrelations further validate the suitability of the second-order latent variable as a reflective indicator (Jarvis et al. 2003). This modeling approach aligns with the characteristics of reflective indicators, providing a deeper understanding of the latent structure of diffusion of innovations as a complex concept.

The model fit indices indicate a good model fit: χ2/df = 1.077, NFI = 0.976, RFI = 0.971, CFI = 0.998, RMSEA = 0.012, and SRMR = 0.028. Additionally, reliability analysis results demonstrate strong internal consistency, with an overall Cronbach’s alpha of 0.883, while reliability values for subdimensions range between 0.8 and 1. This confirms the scale’s internal consistency. With respect to standardized factor loadings, all values exceed 0.6, while AVE values surpass 0.5, and CR values are greater than 0.7, confirming the model’s internal consistency and convergent validity. Additionally, the findings meet the Fornell-Larcker criterion, reinforcing the model’s discriminant validity and affirming the suitability of the reflective measurement approach (Kline, 2023; Pattnaik, 2019). In accordance with the recommendations of Hair et al. (2010), the skewness and kurtosis values remain within the acceptable limits, confirming that the data meet the normality assumption. These findings establish a robust foundation for subsequent model analysis, reinforcing the applicability of the reflective model.

To ensure the validity of interactions among second-order factors, this study conducted a detailed assessment of multicollinearity and model robustness. The results indicate that variance inflation factors (VIF) for all variables are below 3, and the correlation coefficients between first-order and second-order variables are significantly below 0.7 (see Table 3), suggesting that multicollinearity has a negligible impact on the model results (Kock and Lynn, 2012). Additionally, the Durbin-Watson statistic of 1.771, close to the ideal value of 2, confirms the absence of significant autocorrelation, further validating the model’s robustness. The model’s R² value is 0.471, aligning with the standard threshold for explanatory power in social science research (Gefen et al. 2000). These analytical results provide strong theoretical support and empirical evidence for the model’s construction and interpretation.

Table 3 Outcomes of the confirmatory factor analysis.
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Relevance analysis

The correlation analysis in Table 4 shows that compatibility and relative advantage are positively correlated with perceived usefulness, perceived ease of use, and flow experience, while complexity is negatively correlated with these variables. Perceived usefulness, perceived ease of use, and flow experience also showed positive correlations with each other, initially validating the relationship hypothesis. Further analyses showed that the standardized coefficients between the variables were lower than the square root of the corresponding AVE values, demonstrating the good discriminant validity of the measure. These results not only confirmed the validity of the theoretical model but also verified the accuracy and reliability of the data, providing a theoretical and empirical basis for subsequent research.

Table 4 Discriminant validity and correlation analysis.
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Structural modeling analysis

This study utilized SEM to investigate the interrelationships among various variables within the SOR framework, IDT, and ETAM. The key factors of innovation diffusion (compatibility, comparative advantage, and complexity) served as the independent variables, perceived ease of use, perceived usefulness, and flow experience were the mediator variables, and intention to use was the dependent variable. The overall fitness assessment of the model indicated a good fit to the data with the following metrics: χ2/df = 1.088, NFI = 0.950, RFI = 0.946, CFI = 0.996, RMSEA = 0.013, and SRMR = 0.324. The results of the path analyses in Fig. 2 and Table 5 showed that the p-values of all the core hypotheses (H1 through H6) were less than 0.05, confirming the validity of the study hypotheses.

Fig. 2
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Structural modeling result.

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Table 5 Outcomes from testing the hypotheses.
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In order to explore the mediating role of perceived ease of use, perceived usefulness, and flow experience on adoption intentions, this study executed 5,000 sample iterations and set the confidence interval to 95%. The analysis results in Table 6 indicate that perceived ease of use, perceived usefulness, and flow experience partially mediate the relationship between the innovation diffusion of DLP and maritime and logistics organizations.

Table 6 Outcomes of mediation analysis.
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Results and discussion

The results of this study demonstrate that innovation diffusion in DLP has a significant effect on perceived usefulness, perceived ease of use, and flow experience, with all relationships achieving statistical significance. The path analysis further indicates that the impact coefficients for innovation diffusion on perceived usefulness, perceived ease of use, and flow experience are 0.520, 0.670, and 0.710, respectively. Notably, flow experience, as an emotion-driven factor, enhances intention to use by increasing user engagement and immersion (Csikszentmihalyi et al. 2014). These results highlight the notable influence of the diffusion of innovations in shaping users’ technological cognition and emotional experiences.

In existing literature, diffusion of innovations is regarded as a key determinant of successful technology adoption, influencing users’ adaptability and acceptance of new technologies (Davis, 1989; Venkatesh and Bala, 2008). This study further supports the aforementioned theoretical framework and finds that diffusion of innovations not only influences users’ technology acceptance by enhancing perceived usefulness and perceived ease of use but also potentially enhances user intention to adopt by positively affecting flow experience (i.e., user engagement and immersion). Specifically, diffusion of innovations enhances perceived usefulness by improving compatibility and relative advantage, while reducing complexity fosters perceived ease of use. Moreover, by offering a more challenging and interactive user experience, it strengthens the flow experience, thereby potentially enhancing users’ intention to use the platform (Csikszentmihalyi et al. 2014). Therefore, the diffusion of innovations plays a multifaceted role in promoting technology adoption.

The study also reveals that users’ perception of technology acceptance acts as a significant mediator between the diffusion of innovations and intention to use. Specifically, diffusion of innovations exerts a significant indirect effect on the intention to use through perceived usefulness (b = 0.244, P < 0.01, CI = 0.136 ~ 0.377), perceived ease of use (b = 0.241, P < 0.05, CI = 0.061 ~ 0.431), and flow experience (b = 0.347, P < 0.05, CI = 0.132 ~ 0.565). Particularly, when users experience immersive interaction and a sense of self-efficacy within the DLP, they are more likely to develop positive usage intentions. Platform design that facilitates flow state by providing a challenge-skill balance enhances user engagement and immersion, ultimately potentially enhancing the intention to use (Tiwana, 2013). These findings confirm that the diffusion of innovations not only directly enhances users’ technological perceptions but also indirectly facilitates technology adoption through perceived usefulness, perceived ease of use, and flow experience as mediating variables. This outcome not only supports the existing TAM framework but also indicates that integrating flow experience into the TAM framework might extend its applicability. More specifically, flow experience acts as a mediating factor between innovation diffusion and technology adoption, suggesting that greater user immersion and engagement enhance the likelihood of forming a positive intention to adopt the technology (Csikszentmihalyi et al. 2014).

Experimental results further demonstrate that compatibility, relative advantage, and complexity within the diffusion of innovations significantly impact DLP adoption by influencing perceived ease of use, perceived usefulness, and flow experience. These results validate that compatibility, relative advantage, and complexity are significant factors influencing technology adoption. While complexity may hinder acceptance, its effect is moderated by industry-specific, cultural, and policy-related factors, leading to a constrained impact (Russo, 2024). Users’ intention to adopt technology strengthens when they perceive it as both useful and easy to use. Additionally, satisfaction and continued engagement further enhance this adoption intention. Additionally, flow experience within DLP is associated with user retention and adoption rates, underscoring the relevance of emotion-driven factors in technology acceptance. The integration of IDT and ETAM provides a novel perspective on the dynamic process of technology adoption, expanding the theoretical foundation for the diffusion of innovations and user engagement research.

Conclusions

Theoretical contributions

This research provides multiple theoretical insights into the factors influencing DLP adoption intentions within the maritime and logistics sector.

First, it extends the applicability of the SOR framework within the maritime logistics sector by integrating it with the IDT and the ETAM. This integration constructs a comprehensive theoretical framework to examine the key drivers of DLP adoption. The findings indicate that diffusion of innovations, as an external stimulus (S), influences an individual’s internal perception (O) and ultimately shapes their behavioral response (R) (Holland and Gutiérrez-Leefmans, 2018). Specifically, this study is the first to combine the SOR framework, IDT, and ETAM in the context of the maritime and logistics industry, developing a multilayered second-order SEM. The empirical validation of the SOR mechanism in DLP adoption reveals how diffusion of innovations (compatibility, relative advantage, and complexity), as an external stimulus (S), affects perceived usefulness, perceived ease of use, and flow experience (O), ultimately enhancing intention to use (R). This study emphasizes the dynamic interaction between the diffusion of innovations and users’ psychological states, addressing a gap in prior research regarding emotional perception factors in DLP adoption and advancing the understanding of digital transformation in the maritime and logistics industry.

Furthermore, this study provides an in-depth analysis of key influencing factors and extends the ETAM. The findings reveal that diffusion of innovations (compatibility, relative advantage, and complexity) not only directly impacts intention to use but also exerts mediating effects through perceived usefulness, perceived ease of use, and flow experience (Davis, 1989; Venkatesh and Bala, 2008). Specifically, this study confirms diffusion of innovations as a key driver of technology adoption, demonstrating that it significantly enhances intention to use by improving perceived usefulness, perceived ease of use, and flow experience (Csikszentmihalyi et al. 2014). For the first time, flow experience is incorporated into the ETAM framework, and its impact on technology acceptance is empirically examined. The results indicate that flow experience, as an emotion-driven factor, strengthens user immersion and interactive engagement, thereby increasing DLP user retention and attractiveness, ultimately facilitating technology adoption. By constructing a multilayered causal chain, this study unveils the complex mechanisms underlying DLP adoption. These results not only enhance the TAM framework but also offer fresh theoretical and empirical perspectives on the influence of emotional factors in technology adoption.

A key contribution of this research is the creation of a robust and practical empirical model that accurately elucidates the adoption trajectory of DLP in the maritime and logistics sector. The study demonstrates how diffusion of innovations indirectly influences DLP adoption through perceived usefulness, perceived ease of use, and flow experience, confirming the central mediating role of flow experience in technology adoption. The findings show that diffusion of innovations significantly enhances technology attractiveness through flow experience (b = 0.347, P < 0.05), aligning with the theories of Csikszentmihalyi et al. (2014) and Hsu and Lu (2004) on the impact of flow state on individual behavior. This study broadens the perspective of technology acceptance research and establishes a dynamic decision-making model tailored to logistics enterprises’ technology adoption, offering a practical reference framework for businesses implementing DLP in the future.

Practical contributions

First, the findings indicate that diffusion of innovations, perceived usefulness, perceived ease of use, and flow experience are key determinants of intention to use for DLP. Drawing from these findings, this study suggests enterprise management strategies aimed at increasing DLP adoption and improving user engagement within the maritime and logistics sector. (1) Enhancing technology compatibility and relative advantage: The study reveals that compatibility and relative advantage within the diffusion of innovations significantly influence users’ perceived usefulness and perceived ease of use (Crespo et al. 2013; Wang et al. 2018). To facilitate DLP adoption, enterprises should ensure seamless integration with existing logistics management systems, minimize system transition costs, and optimize technological architecture to enable interoperability across logistics firms. For example, implementing open API technology, allows different logistics enterprises and supply chain partners to integrate DLP efficiently, thereby improving cross-platform interoperability (Orser and Riding, 2018). (2) Enhancing flow experience to optimize user engagement: The findings suggest that flow experience is a key factor in fostering user immersion, which substantially increases the intention to adopt the technology (Csikszentmihalyi et al. 2014). Prior studies also confirm that DLP with an enhanced user experience and seamless interaction models fosters higher retention and continued usage (Hadizadeh et al. 2024). To enhance the flow experience, enterprises should: refine platform design: and develop an intuitive user interface (UI) that ensures smooth navigation, minimizes cognitive load, and enhances user familiarity. Enhance interactivity: leverage AI-driven personalized task recommendations, for example, intelligent transport route suggestions or dynamic pricing strategies based on users' operational history. Provide real-time feedback: utilize data analytics for real-time logistics monitoring and dynamic optimization recommendations, enabling users to make data-driven decisions and improving platform engagement (Wiebe et al. 2014). (3) Improving perceived ease of use and perceived usefulness: perceived ease of use and perceived usefulness are critical factors influencing DLP adoption. The path analysis results show that higher ease of use enhances user acceptance and technological adaptability. Enterprises should: streamline operational processes: reduce unnecessary steps, optimize the navigation system, and improve accessibility to key functions, increasing system efficiency. Provide training and technical support: offer online tutorials, AI-powered customer assistance, and one-on-one technical support to lower learning barriers and facilitate a smoother adoption process.

Furthermore, as a key regulatory authority, the government plays a crucial role in facilitating DLP adoption and driving digital transformation in the maritime and logistics industry. This study proposes the following policy recommendations to accelerate the industry’s transition toward digitalization. (1) Establishing incentive mechanisms to promote DLP adoption: findings indicate that complexity in the diffusion of innovations negatively impacts DLP adoption. Governments can intervene through policy measures to lower technological adoption barriers for enterprises. Launching pilot programs, supporting early-stage DLP trials, and providing technical assistance to accelerate technology diffusion. (2) Advancing digital infrastructure development: the maritime and logistics industry faces challenges in data interoperability, as cross-organizational data sharing is hindered by a lack of standardization and information silos (Klievink et al. 2016). This study suggests that governments: promote DLP standardization and develop industry-wide data exchange standards to ensure seamless information sharing across logistics enterprises. Enhance data security legislation, establish DLP data protection standards to safeguard user privacy, and mitigate data breach risks. (3) Providing workforce training to strengthen digital competency: since perceived ease of use directly influences intention to use, governments can enhance technological adaptability among logistics professionals through digital upskilling programs. For example, implement industry training initiatives: offer online courses and in-person workshops to enhance employees’ proficiency in DLP operations (Chen et al. 2021). Encourage university-enterprise collaborations, and develop digital logistics talent programs to equip professionals with specialized digital skills, supporting industry-wide transformation.

Third, to enhance market competitiveness, maritime and logistics enterprises must develop dynamic capabilities to effectively respond to market fluctuations and technological innovations. This study recommends the following strategic measures for enterprises: (1) Enhancing market sensitivity and technological foresight: maritime and logistics enterprises should conduct regular market research and technology assessments to identify emerging technologies and market opportunities. For example, establish innovation teams to analyze logistics market trends, such as AI-driven intelligent dispatch systems and autonomous freight trucks (Saeedikiya et al. 2024). Strengthen industry collaboration by partnering with technology firms and academic research institutions to create technology-sharing platforms. (2) Enhancing organizational agility for rapid market response: enterprises should adopt agile organizational structures to shorten decision-making cycles and swiftly adapt to market fluctuations. For example, establish rapid decision-making mechanisms to enable enterprises to promptly adjust DLP implementation strategies based on market feedback. Optimize resource allocation by dynamically adjusting technology investment ratios to maximize the benefits of digital transformation (Saeedikiya et al. 2024). (3) Integrating supply chain resources for digital transformation: as a critical technological tool in the maritime and logistics industry, DLP facilitates the integration of transportation, warehousing, freight forwarding, and end customers within the supply chain (Yang and Lin 2024). Develop intelligent logistics networks by leveraging DLP to connect transportation companies, port authorities, and cargo owners, enabling end-to-end supply chain optimization. Utilize advanced data analytics and AI-driven market demand forecasting to enhance resource utilization efficiency and reduce operational costs.

Limitations and future directions

While this study has introduced novel perspectives and empirical evidence for understanding the adoption intentions within the maritime and logistics industry regarding DLP, it acknowledges several limitations that merit further exploration in future research. Identifying these limitations not only clarifies the boundary conditions of this study but also outlines avenues for subsequent research.

Firstly, the geographical limitations of the sampled regions may affect the external validity of this study. Although the data was predominantly collected from mainland China and encompassed a certain degree of heterogeneity across industry types, organizational scales, and managerial levels, the overall sample remains geographically concentrated. Consequently, findings may exhibit applicability biases when generalizing DLP adoption behavior to other countries and regions globally, particularly where considerable differences exist in institutional contexts, cultural dimensions, and technological maturity (Asmussen and Goerzen, 2013). Hence, future research could undertake comparative cross-cultural and cross-institutional analyses based on multinational samples, incorporating variables such as Hofstede’s cultural dimensions, policy intensity indices, and the degree of industrial digitalization, to further assess the applicability and robustness of the integrative theoretical framework proposed in this study. Such an approach would substantially enhance the model’s generalizability and theoretical contribution.

Secondly, the measurement dimensions for emotional and psychological experiences require further refinement and elaboration. Although this study incorporates the construct of “flow experience” to enhance the explanatory power of emotional factors within technology acceptance models, flow experience, as a dynamic and subjective psychological state, likely encompasses more intricate internal mechanisms, including sub-dimensions such as emotional motivation, social belongingness, task challenge, and self-efficacy (Beaudry and Pinsonneault, 2010). The present study has neither conducted an in-depth analysis of these emotional mechanisms nor systematically explored their interactions with cognitive pathways. Consequently, future research could employ multidimensional emotional measurement instruments or mixed-method approaches—such as integrating experiments with in-depth interviews—to comprehensively investigate how emotional variables differentially influence user behavior across various stages (e.g., initial adoption, continued usage, and upgrade migration). This would significantly enhance the systematic understanding of psychological mechanisms underlying user behaviors.

Thirdly, the dynamic characteristics of platform technology have not been adequately integrated into the analytical model. Although this study primarily examines how user-level perceptions and experiential factors influence adoption intention, it does not fully capture the evolving attributes intrinsic to platform technology itself, such as update frequency, functional scalability, system stability, and quality of technical support services. These elements significantly impact industries undergoing digital transformation that heavily depend on system coordination and reliability, such as the maritime and logistics industry (Fleischmann et al. 2016). While their effects on initial adoption behavior might be limited in the short term, from a long-term usage perspective, these technological factors directly influence user satisfaction, loyalty, and sustained adoption intention. Hence, future research should conceptualize “platform technological evolution” as an independent research dimension and construct an analytical framework incorporating variables such as technical service performance, responsiveness to updates, and technological transparency. Such an approach would substantially enrich the understanding of platform-specific factors within technology adoption mechanisms.

Finally, the research design did not sufficiently account for the temporal dynamics of user behavior, lacking a longitudinal perspective. As this study employed cross-sectional data analysis, it cannot effectively capture changes in user attitudes and behavioral evolution across different time points, technological update cycles, or usage stages. Given that technology acceptance behavior typically demonstrates stage-specific and path-dependent characteristics, future research could adopt longitudinal methodologies, such as rolling surveys or behavioral trajectory tracking, to investigate how modifications in platform technology attributes (e.g., version updates and functional enhancements) influence the trajectory of users’ psychological states, satisfaction, and continued usage intention. Additionally, future studies could further explore differences between initial adoption motivations and long-term usage mechanisms, particularly in market environments characterized by platform coexistence or high substitutability, to ascertain whether platforms can sustain user loyalty and market share through continuous experiential improvement and technological support.