Enhancing Port Performance through Digital Transformation: The Role of Networking Capability and Organizational transformation in Indonesian Container Ports

2.1 Networking capability and digital adoption

Networking capability is fundamentally an organization’s ability to build, manage, and utilize relationships with external stakeholders such as partners, suppliers, customers, and regulators.^18–20 This capability facilitates the flow of information, resources, and knowledge essential for innovation and adaptation in dynamic environments. In industries such as maritime, effective networking enables ports to collaborate with multiple actors in the supply chain, aligning operations and sharing best practices, which contributes to operational improvements.²¹ Prior research shows that networking strengthens trust and coordination among multiple stakeholders, which are essential for effective collaboration in complex operations.^22–24

Networking capability supports continuous learning and adaptation by exposing the organization to diverse perspectives and innovations from the external environment.^7,25 In the context of digital transformation, networking helps overcome internal limitations such as knowledge gaps or resource constraints, making it a critical antecedent to successful digital adoption in ports.²⁶

Anning-Dorson ²⁷ shows that network centrality has a significant effect on marketing technology (MarTech) adoption in emerging markets, indicating that firms occupying stronger network positions are better able to access external information, identify technological opportunities, and accelerate the adoption of digital technologies. These findings suggest that network-related resources and inter-organizational connections play an important role in supporting technology adoption. These findings are reinforced by Al Halbusi et al., ²⁸ who show that social media network capability strengthens the positive relationship between social media usage and SMEs’ performance. Al Halbusi et al. ²⁸ emphasize that stronger network capability enhances the effectiveness of digital technology use by enabling firms to leverage social media more productively. Overall, these studies indicate that network-related capability and position facilitate digital transformation by improving access to knowledge, strengthening collaboration, and enhancing the effectiveness of digital technology utilization.

The relationship between networking capabilities and digital adoption can be explained through the perspective of Dynamic Capability Theory by Teece et al.²⁹ In this framework, networking capabilities are viewed as dynamic capabilities that enable organizations to respond to the demands of digitalization. Digital Transformation encompasses the process of integrating digital technology into an organization’s operational and strategic activities to improve efficiency, innovation, and service quality.³⁰ Based on this theoretical foundation and empirical findings, we formulate the following hypothesis:

2.2 Organizational transformation and digital adoption

Organizational transformation refers to an entity characterized by leadership and cultural attributes that promote change, innovation, and employee engagement.⁹ Transformational leaders articulate a compelling vision, inspire motivation, and foster an environment where employees are encouraged to embrace change and challenge the status quo.³¹ Such leadership is critical in digital transformation initiatives, which often encounter resistance due to uncertainty and disruption of established routines.³²

Leaders who empower employees and promote learning cultivate a culture that supports experimentation and continuous improvement, both necessary for integrating complex digital systems.³³ Furthermore, organizational transformation typically exhibit flexible structures and open communication, which facilitate coordination across departments and with external partners.³⁴ This organizational characteristic not only drives acceptance of digital tools but also ensures their effective utilization by aligning digital strategies with organizational goals and employee capabilities.³⁵ Consequently, organizational transformation plays a pivotal role in ensuring that digital adoption translates into meaningful improvements in port performance.³⁶

Research by Kraft et al.³⁷ found that digital transformation in small and medium-sized enterprises in Switzerland plays an important role in accelerating the adoption of digital tools, such as management software and online platforms. These findings are reinforced by Bunjak et al.,³⁸ who emphasize that transformational leadership and shared leadership play an important role in encouraging employees to adopt information technology innovations. A work environment that supports innovation, learning, and change management allows employees to be more open to the use of new technologies. Additionally, Zahra et al.³⁹ highlight that organizational transformation supported by strong leadership and an innovative culture is a key factor in accelerating digital adoption in the industrial sector. Overall, these findings confirm that organizational transformation creates the internal readiness necessary to effectively accept, implement, and utilize digital technology.

From the perspective of Dynamic Capability Theory, organizational transformation can be understood as the ability of an organization to continuously adapt, reconfigure, and develop internal resources and capabilities in response to environmental changes.²⁹ Digital adoption itself encompasses the process of integrating digital technology into an organization’s operational and strategic activities to improve efficiency, innovation, and service quality.³⁰ Therefore, organizational transformation have an advantage in orchestrating the internal changes needed to effectively adopt digital technology. Based on this theoretical foundation and empirical evidence, we formulate the following hypothesis:

2.3 Digital adoption and port performance

Digital adoption encompasses the process of integrating digital technologies into an organization’s operational and strategic activities to enhance efficiency, innovation, and service quality. This process is not limited to the acquisition of technology, but also requires changes in organizational processes, skills, and work culture.⁴⁰ In port operations, digital adoption can be realized through cargo handling automation, real-time tracking, electronic documentation, and the use of data analytics to support decision making.¹⁷ These implementations have direct implications for increasing cargo throughput and user satisfaction, which ultimately strengthen port competitiveness.⁴¹

Several previous studies have examined the relationship between digital adoption and port performance. Subasinghe,⁴² through a systematic literature review, concludes that port digitalization plays an important role in enhancing competitiveness and operational sustainability. Technologies such as automation, the Internet of Things (IoT), and artificial intelligence help ports manage cargo and information flows more efficiently, reduce operational costs, and strengthen collaboration among stakeholders. Digitalization also contributes to achieving sustainability targets. Jiang et al.⁴³ find that port-centric information integration has a significant effect on port performance. The adoption of internal and external information systems improves operational efficiency, strengthens coordination among stakeholders, and accelerates data-driven decision making. In addition, Othman et al.⁴⁴ emphasize that the implementation of smart port practices and advanced technologies supports sustainable port performance by strengthening economic, social, and environmental dimensions. Overall, these findings indicate that digital adoption enhances port performance through improvements in operational efficiency, service quality, competitiveness, and sustainability.

From a theoretical perspective, digital adoption can be understood through the Resource-Based View and Dynamic Capability perspectives, namely as a strategic mechanism that enables ports to optimize and reconfigure their operational resources in alignment with dynamic environmental demands.^29,45 Digital technologies allow ports to improve workflows, accelerate service processes, increase transparency, and strengthen coordination among stakeholders within the port ecosystem. However, the impact of digital adoption on performance is strongly influenced by the readiness of internal organizational capabilities and the external environmental conditions surrounding the port.⁴⁶ Therefore, understanding the drivers of digital adoption is crucial in order to maximize the benefits of digital transformation.^47,48 Based on the discussion above, the following hypothesis is formulated:

2.4 Mediating role of digital adoption

From a capability perspective, organizational capabilities do not automatically result in better performance if they are not realized through the adoption and utilization of digital technology in business processes. Digital transformation is enabled by organizational and managerial capabilities that help firms identify digital opportunities, reconfigure resources, and embed digital technologies into day-to-day operational processes.^49,50 Heredia et al.⁵¹ provide empirical evidence that digital capabilities and organizational readiness contribute to company performance, particularly through digital adoption. This study shows that the benefits of new organizational capabilities can be realized when digital technology is integrated into the operational and strategic activities of the organization. Hadi⁵² emphasizes that organizational capabilities developed through digital transformation initiatives and strategic leadership influence organizational performance through the digital implementation process. This study highlights that digital initiatives serve as channels that enable organizational capabilities to be operationalized effectively. Therefore, performance improvement can also be influenced by the extent to which digital technology is successfully adopted and aligned with organizational processes.

Second, organizational transformation contribute to digital adoption through the creation of an innovative culture, leadership that supports change, and the strengthening of human resource readiness. Yang et al.³² explain that the combination of access to external knowledge and an innovative culture strengthens the integration and utilization of digital technology. In addition, organizational transformational attributes help overcome adoption barriers such as resistance to change and technical skill limitations, thereby increasing the probability of successful digital transformation.^53,54 When organizations successfully adopt digital technology more effectively, port performance improvements become more likely through process, coordination, and service improvements.

Organizations with strong capabilities tend to perform better, mainly because they are more effective in adopting and utilizing digital solutions.^55–57 Thus, understanding the mediating role of digital adoption is important for port managers because improving performance is not enough just by strengthening organizational capabilities, but must also be accompanied by targeted digital initiatives so that these capabilities truly result in operational improvements. Based on the theoretical arguments and empirical findings above, the research hypothesis is formulated as follows:

The research model is illustrated in Figure 1.

Figure 1. Conceptual framework.

4.1 Measurement model evaluation

The Common Method Bias (CMB) test was conducted using Harman’s single-factor test. The results of the analysis indicate that the first single factor explains only 40.58% of the total variance, which is still below the 50% threshold. Thus, the data from this study do not indicate any serious common method bias issues, and it can therefore be concluded that the study meets the CMB criteria. Based on Table 1, the study collected 103 valid responses, with respondents predominantly male (101; 98.06 percent) and a small proportion female (2; 1.94 percent). In terms of positions, the sample consists of senior decision makers and core functional managers, including 19 Directors (18.45 percent), 51 General Managers (49.51 percent), and 33 Managers (32.04 percent). The age distribution is concentrated in the mid to late career groups, particularly 50 to 54 years (35; 33.98 percent) and 45 to 49 years (29; 28.16 percent), followed by 40 to 44 years (14; 13.59 percent), while younger and older groups are less represented. Regarding education, respondents have a high academic profile, with 61 holding doctoral degrees (59.22 percent), 41 holding master’s degrees (39.81 percent), and 1 holding a bachelor’s degree (0.97 percent). Overall, these demographics indicate that the data were obtained from experienced managerial informants with strong educational backgrounds, supporting the credibility of their assessments of networking capability, organizational transformation, digital adoption, and port performance.

This section presents the results of the measurement model evaluation, focusing on the reliability and validity of each construct used in the study. Four main constructs were assessed: Networking Capability, Organizational transformation, Digital Adoption, and Port Performance. To ensure the robustness of the measurement instruments, several key statistical criteria were evaluated, including outer loading, Cronbach’s Alpha, Composite Reliability (rho_A), Composite Reliability (rho_c), and Average Variance Extracted (AVE). Table 2 below shows the results of validity and reliability testing in this study.

Based on Table 2, the evaluation of the measurement model begins with an assessment of indicator validity through outer loading values. In a reflective measurement model, the recommended outer loading threshold is at least 0.70 to ensure that the indicators adequately reflect the underlying construct, while indicators with loadings below 0.70 may be considered for elimination if they adversely affect the reliability and validity of the construct.⁶³ The results indicate that most indicators meet this criterion. However, indicators X1.4, X1.6, X1.8, and X2.2 do not satisfy the recommended threshold. Referring to the guidelines for evaluating reflective measurement models,⁶³ this study conducts instrument purification by removing indicators that fail to meet the minimum outer loading criterion, while still considering conceptual relevance and content validity of the constructs. Referring to the guidelines for evaluating reflective measurement models,⁶³ this study conducted instrument purification by removing indicators that consistently had low outer loadings, after first reviewing the conceptual relevance of the indicators to the core domain of the construct. Thus, the removal of indicators was not done solely for statistical reasons, but was also based on theoretical considerations to maintain the conceptual appropriateness and content validity of the construct, while also improving reliability and convergent validity.

The final results after removing the indicators that do not meet the criteria are presented in Table 3.

After the purification process, the results presented in Table 3 show that all retained indicators have outer loading values ranging from 0.707 to 0.890, indicating that they generally meet the minimum threshold of 0.70 and approach the reference value of 0.708. This suggests that indicator reliability is adequate for all constructs examined. The next step involves assessing internal consistency and convergent validity at the construct level. Hair et al.⁶⁴ explain that internal reliability can be evaluated using Cronbach’s alpha and composite reliability, with general guidelines indicating that values between 0.60 and 0.70 are acceptable for exploratory research, values between 0.70 and 0.90 indicate good reliability, and excessively high values may signal indicator redundancy.⁶⁴ Convergent validity is subsequently assessed using the average variance extracted (AVE), with a minimum recommended threshold of 0.50.⁶⁴ The results in Table 3 indicate that all constructs exhibit Cronbach’s alpha values ranging from 0.702 to 0.926, composite reliability (rho_c) values ranging from 0.834 to 0.944, and AVE values ranging from 0.610 to 0.771. Therefore, following the removal of indicators that did not meet the outer loading criteria, all constructs in the measurement model demonstrate adequate internal consistency and strong convergent validity, confirming their suitability for testing the structural model in the subsequent stage.

Based on Table 4, discriminant validity is evaluated using the Fornell and Larcker criterion, which states that the square root of the AVE on the diagonal should be greater than the inter-construct correlations in the corresponding row and column.^65,66 The results show that the square root of the AVE for X1 is 0.781, which is greater than its correlations with X2 (0.702), Y1 (0.474), and Y2 (0.537). A similar pattern is also observed for X2, Y1, and Y2, where each diagonal value (0.791; 0.878; 0.819) is higher than the correlations with other constructs. These findings indicate that each construct explains the variance of its own indicators better than the variance it shares with other constructs, thus confirming that discriminant validity is established.

Next, Table 5 presents the results of the multicollinearity assessment in the inner model using VIF values. In PLS-SEM reporting, VIF is used to ensure that excessive collinearity does not occur among predictor constructs. VIF values below 3 indicate that there is no multicollinearity issue among the constructs.⁶⁴ Based on Table 5, the VIF values range from 1.000 to 1.973, indicating that the structural model does not encounter multicollinearity problems, and the estimation of path coefficients can be interpreted with an adequate level of reliability.

4.2 Hypothesis testing

Based on Table 6, the results of the direct effects testing show that all proposed hypotheses are empirically supported at the conventional level of significance. First, Networking Capability (X1) has a positive effect on Digital Adoption (Y1), with a path coefficient of 0.255 and a p-value of 0.049. This finding indicates that the stronger a port’s networking capability, the higher the organization’s propensity to adopt digital technologies and systems. Second, Organizational transformation (X2) also has a positive and significant effect on Digital Adoption (Y1), with a path coefficient of 0.312 and a p-value of 0.008. This result shows that organizational transformational characteristics, such as the ability to undertake adaptive internal renewal and change, contribute meaningfully to accelerating digital adoption in the port environment. Third, Digital Adoption (Y1) is shown to have a positive and highly significant effect on Port Performance (Y2), with a path coefficient of 0.535 and a p-value of 0.000. The magnitude of this coefficient indicates that digital adoption is an important determinant of improved port performance, thereby strengthening the argument that digitalization in port operational and service processes plays a direct role in driving organizational performance.

Based on Table 7, the indirect effects testing was conducted to assess the mediating role of Digital Adoption (Y1) in the relationship between the independent variables and Port Performance (Y2). The analysis results indicate that the indirect effect of Networking Capability (X1) on Port Performance (Y2) through Digital Adoption (Y1) has a path coefficient of 0.136 with a p-value of 0.069. This p-value exceeds the 0.05 significance level; therefore, hypothesis H4 is not empirically supported. In contrast, the results show that Organizational transformation (X2) has a significant indirect effect on Port Performance (Y2) through Digital Adoption (Y1), with a path coefficient of 0.167 and a p-value of 0.011. This finding supports hypothesis H5 and indicates that Digital Adoption serves as a significant mediator in the relationship between organizational transformational characteristics and port performance. These results imply that an organization’s capability to undertake internal change, renewal, and transformation fosters more effective digital adoption, which in turn positively contributes to improved port performance.

4.3 Discussion

The results of hypothesis testing show that networking capability has a significant effect on digital adoption, thus supporting H1. This finding indicates that the port’s ability to build, manage, and utilize networks with various external stakeholders plays an important role in driving digital technology adoption. The stronger the organization’s networking capability, the greater its ability to effectively adopt digital technology in its operational and strategic activities.

These findings are in line with the study by Waty et al.,²⁷ which shows that networking capabilities have a significant effect on digital adoption and business agility. The study confirms that organizations with strong networking capabilities are better able to adopt new technologies quickly and effectively because networks expand access to the latest technological innovations and accelerate knowledge exchange with business partners. These results are also consistent with Al Halbusi et al.,²⁸ who found that networking capabilities, particularly through social media networks, have a significant effect on social media adoption. The study emphasizes that customer engagement and the effectiveness of social networks strengthen the use of digital platforms as business tools. From the perspective of Dynamic Capability Theory,²⁹ networking capability can be understood as a dynamic capability that helps organizations respond to the demands of digitalization.

The test results show that organizational transformation have a significant effect on digital adoption, thus supporting H2. These findings confirm that port organizations with transformational characteristics, as reflected in visionary leadership, innovative culture, and employee engagement, tend to be more capable of effectively adopting digital technology. Theoretically, these results are consistent with the view that transformational leaders are able to build a strong vision, inspire motivation, and create a work environment that encourages employees to accept change and challenge outdated practices that are no longer relevant.^9,31 This is important in digital transformation initiatives, which generally face resistance due to uncertainty and disruption of established work routines.³²

The empirical findings of this study are in line with the study by Kraft et al.,³⁷ which shows that organizational transformation facilitates the adoption of digital tools such as management software and online platforms. These results are also consistent with Bunjak et al.,³⁸ who emphasize that transformational leadership and shared leadership play an important role in encouraging the adoption of information technology innovations by employees through the creation of a work environment that supports innovation, learning, and change management. Furthermore, Zahra et al.³⁹ highlight that organizational transformation supported by strong leadership and an innovative culture is a key factor in accelerating digital adoption in the industrial sector. The alignment of these research findings with previous studies reinforces the argument that organizational transformationalism acts as a key enabler for digital adoption. From the perspective of Dynamic Capability Theory,²⁹ organizational transformation reflects the ability to continuously adapt, reconfigure, and develop internal resources and capabilities in response to environmental changes.

The results of this study also found that digital adoption has a significant effect on port performance, thus supporting H3. These findings indicate that the higher the level of digital technology adoption in port operations, the better the port performance achieved. This relatively strong influence confirms that digitalization is not merely an operational complement, but a strategic factor that directly contributes to improving port efficiency, service quality, and competitiveness. These findings are in line with Subasinghe,⁴² who, through a systematic literature review, concluded that port digitalization plays an important role in improving competitiveness and operational sustainability. The use of technologies such as automation, the Internet of Things, and artificial intelligence has been proven to help ports manage the flow of goods and information more efficiently, reduce operational costs, and strengthen collaboration between stakeholders. These results are also consistent with Jiang et al.,⁴³ who found that port-based information integration has a significant effect on port performance through improved operational efficiency, coordination between actors, and data-driven decision-making. Furthermore, Othman et al.⁴⁴ emphasized that the implementation of smart port practices supports sustainable performance improvement by strengthening economic, social, and environmental dimensions.

From the Resource-Based View and Dynamic Capability perspectives, these findings reinforce the view that digital adoption constitutes a strategic mechanism for optimizing and reconfiguring port operational resources to align with dynamic environmental demands.^29,45 Digital technologies enable ports to improve resource-use efficiency, enhance workflows, and strengthen coordination within a complex port ecosystem. However, these results also indicate that the benefits of digital adoption for performance are not automatic, but rather depend heavily on the readiness of an organization’s internal capabilities and the external environmental context faced by the port.⁴⁶

The non-significant indirect effect of Networking Capability on Port Performance through Digital Adoption (H4) suggests that, in the port context, external relational ties do not automatically translate into measurable improvements in digital uptake and subsequent operational outcomes. By contrast, the significant mediation of Digital Adoption for Organizational transformation (H5) is theoretically coherent because internal change capability, renewal routines, and leadership-driven reconfiguration enable organisations to mobilise resources, redesign processes, and institutionalise new digital work practices that are tightly coupled with performance. This pattern differs from information-intensive service sectors such as healthcare. In this sector, organizational networks and digital capabilities more readily accelerate digital adoption and improve service performance. Evidence from Indonesian hospitals also shows that digital adoption capability influences performance and is shaped by environmental dynamism and relevant capability antecedents.^67,68 In general, these findings indicate that the relationship between digital adoption and performance is contextual, so testing the model in different sectors can strengthen generalizations and enrich the theory.