The dynamic capability theory provides an overarching theoretical foundation for this research, offering insights into how organisations detect environmental changes, capitalise on them and realign their resources (Teece 2007; Teece et al. 1997). The dynamic capability theory aligns with the resource-based theory (RBT), which states that competitive advantage results from valued, scarce, non-imitable and organised resources (Barney, Ketchen & Wright 2021). While RBT focuses on resource ownership, dynamic capability theory explains how firms sustain competitiveness by renewing and redeploying resources in changing environments. This perspective is particularly relevant to SOEs, where the deployment of dynamic capabilities is institutionally complex. State-owned enterprises must balance commercial objectives with public, political, regulatory and socio-economic mandates while maintaining legitimacy, accountability, transparency and political control (Larsen, Volden & Andersen 2021). In Indonesia, SOEs play important roles in providing public goods and services, supporting government programmes, creating employment, assisting small businesses and contributing to government revenue (Jahja et al. 2020). However, Indonesian SOEs may also face bureaucratic procedures, governance complexity, limited managerial discretion, soft-budget constraints, policy burdens, agency problems and weaker incentives for innovation and efficiency (Arif & Sule 2019; Astami et al. 2010; Jahja et al. 2020). This creates a theoretical tension: government support and institutional legitimacy may provide advantages, but bureaucratic and institutional constraints may weaken strategic responsiveness and innovation. In state-owned logistics firms, this tension becomes more salient. Logistics firms operate in a sector shaped by digital transformation, rising customer expectations for speed and reliability and pressure to improve operational efficiency (Dovbischuk 2021; Kuteyi & Winkler 2022; Verhoef et al. 2021). At the same time, state-owned logistics firms must respond to market and technological changes while maintaining public service responsibilities, regulatory compliance and accountability to government stakeholders (Larsen et al. 2021; Uyar, Fernandes & Kuzey 2021). Because logistics performance is influenced by governance quality, infrastructure, technological adoption and reliable service delivery, performance improvement depends not only on state resources or institutional support but also on the capability to sense market changes, reconfigure service delivery systems and renew business models (Teece 2007; Uyar et al. 2021). Accordingly, SOE performance cannot be explained solely by resource ownership or government support. It depends on whether SOEs can deploy dynamic capabilities to transform environmental change and institutional constraints into adaptive strategic action. Under such circumstances, strategic agility can be viewed as the sensing and seizing element of dynamic capabilities, BMI as the reconfiguration element and absorptive capacity as the knowledge processing capability that facilitates sensing, learning and reconfiguration through acquiring, assimilating, transforming and exploiting knowledge from outside sources (Cohen & Levinthal 1990; Zahra & George 2002). Thus, this article conceptualises strategic agility as an adaptive capability, BMI as the mechanism by which agile responses are converted into performance outcomes and absorptive capacity as the boundary condition that helps convert strategic agility into BMI in SOEs engaged in logistics.

Strategic agility refers to an organisation’s ability to sense environmental changes, respond flexibly to market and technological shifts and realign strategic actions and resources accordingly (Adomako et al. 2022; Clauss et al. 2021; Liu et al. 2023b). From the dynamic capability perspective, strategic agility enables firms to translate environmental sensing into timely strategic responses. This capability becomes increasingly important in turbulent and digitalised environments, where firms must continuously adjust strategic priorities, resource allocation and operations (Tallon & Pinsonneault 2011; Teece 2007; Troise et al. 2022). Firm performance in this study refers to the extent to which firms achieve market, financial and operational outcomes, including sales growth, service quality, productivity, delivery reliability and competitive position (Rajapathirana & Hui 2018; Tajvidi & Karami 2021). Prior studies generally suggest that strategic agility enhances FP because agile firms are more responsive to environmental change, better able to improve internal processes and more capable of sustaining competitiveness (AlTaweel & Al-Hawary 2021; Arokodare & Ph 2021; Yildiz & Aykanat 2021). These findings are especially relevant in dynamic markets characterised by uncertainty, digitalisation and competitive pressure (Ahammad et al. 2021; Jafari-sadeghi et al. 2022; Troise et al. 2022). However, the strategic agility–performance relationship should not be treated as automatic. Although speed, flexibility, and responsiveness are important, they may generate limited performance gains if agile actions remain confined to short-term operational or administrative adjustments rather than being translated into substantive resource reconfiguration, service renewal and BMI (Clauss 2016; Clauss et al. 2021; Teece 2010). This issue is particularly relevant in state-owned logistics firms because strategic responsiveness is shaped not only by market pressures but also by governance structures, public service obligations, bureaucratic procedures and ownership-related constraints. These conditions may hinder the translation of strategic responses into performance improvements, as prior studies show that public and corporate governance, bureaucratic barriers, infrastructure limitations and financing constraints significantly affect logistics performance (Kuteyi & Winkler 2022; Liu et al. 2023a; Uyar et al. 2021). Moreover, logistics performance is not limited to financial results but also includes delivery reliability, delivery speed, service responsiveness, operational productivity and digital service capability. Prior studies show that indicators such as timeliness, order accuracy, complaint handling, real-time tracking and information transparency are central to logistics competitiveness in digital and e-commerce-based environments (Al-Shboul 2022; Li et al. 2022; Wu et al. 2023). Therefore, the effect of strategic agility on FP requires further examination in the context of state-owned logistics firms:

H1: Strategic agility has a positive and significant effect on firm performance.

The next subsection extends this argument by examining whether strategic agility leads to BMI, which represents a more substantive form of strategic renewal.

Business model innovation refers to changes in the logic of value creation, value delivery and value capture (Clauss 2016; Teece 2010). Compared with product, process or operational innovation, BMI involves a broader redesign of the firm’s value proposition, activity system, revenue logic and cost structure (Chen, Liu & Wang 2021; Clauss 2016; Clauss et al. 2021). From a dynamic capability perspective, BMI reflects the reconfiguration of resources and business logic that enables firms to adapt to changing market and technological conditions. Strategic agility is expected to foster BMI because agile firms are more capable of detecting environmental shifts, making timely decisions, and reallocating resources towards emerging opportunities (Adomako et al. 2022; Clauss et al. 2021; Troise et al. 2022). Prior studies suggest that strategically agile organisations are more likely to renew their business models because agility enables firms to modify their value creation, value delivery and value capture mechanisms in response to external change (Albayraktaroglu 2024; Clauss et al. 2021; Yildiz & Aykanat 2021). Nevertheless, not all agile responses result in BMI. Some agile actions may remain limited to temporary administrative adjustments, incremental process improvements or short-term operational responses, whereas BMI requires deeper reconfiguration of the firm’s value proposition, activity configuration, service delivery and value capture logic (Clauss 2016; Clauss et al. 2021; Teece 2010). This distinction is particularly important in state-owned logistics firms, where bureaucratic procedures, regulatory controls, public service obligations and ownership-related constraints may limit the extent to which strategic responsiveness can be converted into substantive business model renewal. In logistics services, business model renewal may involve redesigning service delivery systems, improving digital tracking and information transparency, strengthening last-mile delivery, expanding partner networks and developing more customer-oriented logistics solutions as part of broader changes in value creation, value delivery and value capture mechanisms (Clauss et al. 2021; Kuteyi & Winkler 2022; Teece 2010). Thus, the gap lies in explaining whether strategic agility in state-owned logistics firms produces substantive BMI rather than merely operational flexibility. This study argues that strategic agility provides the adaptive basis for BMI by enabling state-owned logistics firms to convert environmental responsiveness into renewed value creation, value delivery and value capture mechanisms:

H2: Strategic agility has a positive and significant effect on business model innovation.

With the identification of strategic agility as an antecedent of BMI, the next subsection examines the possibility of the influence of FP on BMI.

Business model innovation contributes to FP by enabling firms to align their business logic with changing customer needs, technological developments and competitive conditions. Through the redesign of value propositions, activity systems, revenue models and cost structures, firms can transform existing resources and capabilities into renewed sources of value and competitive advantage (Chen et al. 2021; Clauss et al. 2021; Teece 2010). Most of the previous literature suggests that BMI enhances FP by increasing customer value, improving operational efficiency, creating new revenue streams and achieving competitive advantage (Menter et al. 2023; Salfore, Ensermu & Kinde 2023; Tajvidi & Karami 2021). This is particularly relevant in the logistics and service industries, where digitalisation, service reliability, customer responsiveness and competitive pressures require firms to renew not only their operations but also their overall business logic (Bogale, Birbirsa & Abegaz 2025; Dovbischuk 2021). However, the influence of BMI on FP should not be assumed to be automatic. According to Latifi, Nikou and Bouwman (2021), the association between BMI and FP was rendered insignificant when efficiency, revenue generation and organisational abilities were used as mediating variables. This suggests that BMI improves performance when business model changes are effectively translated into efficiency gains, revenue growth and capability development. Thus, BMI should be viewed not merely as an innovation activity but as a reconfiguration process whose performance effects depend on implementation and contextual conditions. This issue is particularly important in state-owned logistics firms, where bureaucratic routines, public mandates, regulatory constraints and limited managerial autonomy may restrict the extent to which business model changes can be implemented (Kuteyi & Winkler 2022; Uyar et al. 2021). On the one hand, BMI may help these firms improve customer value, service delivery, operational efficiency, delivery reliability and financial outcomes by reconfiguring the mechanisms of value creation, value delivery and value capture (Clauss 2016; Clauss et al. 2021; Teece 2010). On the other hand, institutional constraints may reduce the speed and depth of business model renewal, making its performance effect dependent on effective implementation and contextual conditions (Latifi et al. 2021). Therefore, this study addresses the gap in understanding whether BMI enhances FP in state-owned logistics firms, where performance improvement may require more than incremental operational change:

H3: Business model innovation exerts a positive and statistically significant influence on firm performance.

Building on the expected effects of strategic agility on BMI and BMI on FP, the next subsection examines the mediating role of BMI.

Strategic agility is expected to improve FP both directly and indirectly through BMI. From the dynamic capability perspective, sensing and seizing capabilities do not automatically generate superior performance unless they are followed by effective reconfiguration (Teece 2007). In this study, BMI represents the reconfiguration mechanism through which agile responses are transformed into renewed business logic and performance-enhancing outcomes. Business model innovation is positioned as the central mediating mechanism because it captures how firms redesign the logic of value creation, value delivery and value capture. Although digital capability, operational flexibility and innovation capability are important for explaining adaptation and performance, they do not fully explain how strategic responsiveness is converted into renewed business logic (Bharadwaj 2000; Lawson & Samson 2001; Tallon & Pinsonneault 2011). Business model innovation is therefore more relevant to this study because it explains how firms reconfigure value propositions, activities, partners, cost structures and revenue mechanisms to generate performance outcomes (Clauss et al. 2021; Teece 2010; Zott & Amit 2010). Although Clauss et al. (2021) demonstrated that BMI mediates the relationship between strategic agility and FP, their study focused on firms in the electronics industry and examined environmental turbulence as the main boundary condition. This leaves an important gap in understanding whether the same mechanism operates in state-owned logistics firms, where strategic responsiveness may be constrained by bureaucratic procedures, public service obligations, regulatory control and limited managerial autonomy. This mediating logic is particularly important in state-owned logistics firms because such institutional constraints can weaken the conversion of strategic agility into performance-enhancing outcomes. Under these conditions, agile responses are likely to remain symbolic, administrative or operational unless they are translated into substantive business model renewal through changes in value creation, value delivery and value capture mechanisms (Clauss et al. 2021; Kuteyi & Winkler 2022; Teece 2010; Uyar et al. 2021). Therefore, this study argues that BMI serves as the mediating mechanism linking strategic agility to improved market, financial and operational outcomes in state-owned logistics firms:

H4: Business model innovation mediates the relationship between strategic agility and firm performance.

Although BMI explains how strategic agility affects performance, the effectiveness of the agility–innovation conversion may depend on the firm’s ability to process and use external knowledge. This issue is addressed through the moderating role of absorptive capacity.

Although prior research has shown that environmental turbulence strengthens the relationship between strategic agility and BMI, it primarily captures external pressure for business model renewal (Clauss et al. 2021). It does not fully explain whether firms possess the internal knowledge-processing capability required to convert agile responses into substantive BMI. This study, therefore, extends prior research by positioning absorptive capacity as a moderating capability that strengthens the relationship between strategic agility and BMI. Absorptive capacity refers to the ability of an organisation to absorb, assimilate, convert and utilise external knowledge to facilitate learning and innovation (Cohen & Levinthal 1990; Zahra & George 2002). This capability comprises potential absorptive capacity, which involves knowledge absorption and acquisition, and realised absorptive capacity, which involves knowledge conversion and utilisation (Zahra & George 2002). This distinction is important because external knowledge does not automatically lead to innovation unless firms can recognise, interpret, transform and apply it to renew their business models. Prior studies have commonly positioned absorptive capacity as an antecedent of innovation, agility or performance, while others have treated it as a mediating mechanism through which external knowledge is transformed into competitive advantage or performance outcomes (Bakkar, Li & Nafizur 2023; Bhatti et al. 2021; Makhloufi et al. 2021; Tufan & Mert 2023). While these perspectives are valuable, they offer only a limited explanation of why some agile firms is more successful than others at converting strategic responsiveness into BMI. Therefore, this research offers an alternative perspective on absorptive capacity as a moderating variable. Strategic agility enables firms to respond effectively to environmental changes, but absorptive capacity determines how much external knowledge they use to create new value. The moderating role of absorptive capacity is particularly important in state-owned logistics firms, where institutional constraints may limit experimentation and slow the use of external knowledge in redesigning business models. In this context, absorptive capacity can strengthen the agility–innovation link by ensuring that agile responses are informed by relevant market, technological and customer knowledge and translated into meaningful business model reconfiguration (Cohen & Levinthal 1990; Kuteyi & Winkler 2022; Uyar et al. 2021; Zahra & George 2002). This role becomes more important under conditions of digitalisation, complex customer needs, service reliability demands and strong institutional constraints, where firms require richer external knowledge to renew their business models (Teece 2007; Verhoef et al. 2021). Therefore, absorptive capacity is positioned as a boundary condition rather than merely as another antecedent of BMI. Firms with stronger absorptive capacity are expected to convert strategic agility into BMI more effectively because they can acquire, interpret, transform and exploit external knowledge in renewing their business models:

H5: Absorptive capacity moderates the relationship between strategic agility and business model innovation, such that the positive influence of strategic agility on business model innovation is conditional on a certain level of absorptive capacity.

Drawing on the theoretical foundation and prior empirical findings, the conceptual model is presented in Figure 1.

A questionnaire-based survey was administered via Google Forms, with data collected between August and October 2024. The research focused on an SOE in the logistics sector operating on Java Island, with branch offices distributed across several provinces, including DKI Jakarta, West Java, Central Java and East Java. Java Island was chosen as the research setting because of its significantly higher productivity and economic contribution compared with other regions in Indonesia, providing a relevant context for analysing managerial decision-making and FP in the logistics industry. The respondents were managerial-level employees with the authority to make strategic decisions and drive BMI within their respective organisational units. The total population of managerial employees operating on Java Island was 3416. Based on the Krejcie and Morgan (1970) formula, the minimum required sample size was 345 respondents. A total of 427 valid responses were obtained, indicating that the sample size exceeded the minimum requirement and was sufficient to represent the target population. A proportionate stratified random sampling technique was employed, with internal organisational units as strata. This procedure was adopted to ensure balanced representation across the company’s structural divisions within these operational areas. This approach strengthened the structural representativeness of the sample while maintaining consistency within a relatively homogeneous governance and management framework. The study began with a brief introductory page outlining the research objectives and informing respondents that participation was voluntary and anonymous. No personal identifiers were collected, and the information was used solely for research purposes in accordance with established ethical guidelines. Given this research design, the relationships examined in the structural model should be interpreted as associational rather than strictly causal. Although the proposed model is theoretically grounded in dynamic capability theory and prior empirical research, the cross-sectional data gathered using self-report surveys make it challenging to determine the temporal ordering of the constructs. Consequently, the findings indicate the strength and direction of statistical relationships among strategic agility, BMI, absorptive capacity and FP, but they do not provide definitive evidence of causality.

Table 1 shows that the most participants had held their current positions for 1–5 years (55.3%), followed by those with less than 1 year of tenure (30.7%), suggesting a relatively recent distribution of role assignments. In contrast, overall work experience was substantial, with 72.6% of respondents having more than 10 years of service, indicating strong organisational familiarity and accumulated experience. In terms of educational background, the majority of respondents held a diploma or a bachelor’s degree (55.3%), while 38.6% had a senior high school or vocational education, and only a small proportion held postgraduate qualifications. The sample was predominantly male (74.0%), with 26.0% female respondents. Regarding age, most respondents were in the 31 years to 50 years range (71.2%), followed by those aged 50+ (25.3%), reflecting a mature, experienced workforce capable of providing informed assessments of organisational practices and performance.

Absorptive capacity, strategic agility, BMI and FP were measured using items adapted from well-established scales in prior empirical research to ensure content validity and theoretical consistency. Each item was evaluated using a five-point Likert scale, with response options ranging from 1 (strongly disagree) to 5 (strongly agree). Absorptive capacity was measured using eight items that capture knowledge acquisition, assimilation, transformation and application, adapted from Sheng and Chien (2016), Miroshnychenko et al. (2021), Ma et al. (2021), Dabić et al. (2023) and Khan, Lew and Marinova (2019). Business model innovation was operationalised with 10 items reflecting customer orientation, value creation, technological opportunities, pricing strategies and cost efficiency, primarily adapted from Clauss et al. (2021). Strategic agility was assessed using eight items that capture sensing, responsiveness, flexibility and rapid organisational adjustment, adapted from Clauss et al. (2021), Adomako et al. (2022) and Liu et al. (2023b). Firm performance was measured using six subjective indicators reflecting sales growth, service quality improvement, revenue growth, delivery reliability and productivity, adapted from Latifi et al. (2021), Rajapathirana and Hui (2018), Tajvidi and Karami (2021), Alsheyadi (2022) and Wang, Wang and Liang (2014). Subjective FP measures were used because comparable objective financial and operational key performance indicator (KPIs) at the respondent or organisational-unit level were not publicly available and could not be accessed because of organisational confidentiality restrictions. In addition, objective performance data were not available in a consistent, comparable format across branches, making direct cross-unit comparisons less straightforward. This condition is consistent with prior performance-measurement research, which notes that objective performance data may be difficult to obtain, confidential, aggregated at a higher organisational level or not aligned with the unit of analysis (Dess & Robinson 1984; Wall et al. 2004). Yildiz and Karakaş (2012) also noted that objective performance data are often difficult to obtain because firms generally do not wish to disclose such information. Prior studies have also shown that perceptual or subjective performance assessments can serve as valid proxies for objective performance when objective indicators are unavailable or difficult to obtain, particularly when the measures are adapted from established scales and reported by knowledgeable managers (Dess & Robinson 1984; Venkatraman & Ramanujam 1986; Wall et al. 2004). Therefore, subjective performance measures were considered appropriate in this study because the respondents were managerial-level employees with sufficient knowledge of strategic decisions, innovation activities and operational outcomes within their respective units. This decision was further supported by the respondent profile, in which 72.6% had more than 10 years of work experience, and 55.3% had held their current managerial positions for 1–5 years, indicating sufficient organisational familiarity and managerial exposure to provide informed performance assessments. A pilot study with 30 managerial-level respondents was conducted to assess item clarity, wording and internal consistency reliability. Based on the pilot results, minor refinements were implemented to improve readability and ensure the questionnaire’s suitability for the main survey.

Ethical clearance to conduct this study was obtained from the Universitas Brawijaya Faculty of Medicine Health Research Ethics Committee (No. 285/KEPK/09/2024), received 05 August 2024.

The results of the PLS-SEM analysis in this study are presented in Figure 3, which illustrates the measurement and structural model, including the relationships among the constructs and their respective indicators.

The assessment of the measurement model is summarised in Table 2. Based on Table 2, item BM10 has a factor loading slightly below the recommended threshold of 0.70, at 0.699. However, the item was retained because its loading was very close to the recommended threshold, and the corresponding construct met the recommended criteria for CR (> 0.60) and AVE (> 0.50). In accordance with Hair et al. (2021), indicators with loadings between 0.40 and 0.70 should not be automatically eliminated; instead, their removal should be considered only when it improves internal consistency reliability or convergent validity while also taking content validity into account. Therefore, BM10 was retained in this study. In addition, all VIF values were below the commonly accepted threshold of 5.0, suggesting that indicator collinearity was not a serious concern. Overall, based on the factor loadings, CR, AVE and VIF values, all indicators meet the criteria for indicator reliability, internal consistency and convergent validity, while collinearity among indicators is adequately controlled within acceptable limits. Consequently, all indicators were deemed suitable for further analysis.

Discriminant validity was assessed using the HTMT. The HTMT values should be below 0.90 to indicate adequate discriminant validity. As presented in Table 3, all HTMT values are below the recommended threshold, indicating that the constructs are empirically distinct and that the discriminant validity requirement has been satisfactorily achieved.

The evaluation of the inner model was performed using R² values and SRMR. The structural model evaluation relied on the R² values shown in Table 4.

The R² value of 0.673 for BMI demonstrates substantial explanatory power, while the value of 0.465 for FP indicates moderate explanatory power. These results suggest that the model is sufficiently robust for hypothesis testing. The SRMR value of 0.060 in this study demonstrates that the model achieves an acceptable level of fit. The significance of the estimated path coefficients was used to evaluate the proposed hypotheses, as summarised in Table 5.

Table 5 shows that strategic agility is positively and significantly associated with FP (β = 0.259; p < 0.001), supporting H1. Strategic agility is also positively and significantly associated with BMI (β = 0.637; p < 0.001), supporting H2. In addition, BMI is positively related to FP (β = 0.455; p < 0.001), providing support for H3. Mediation analysis indicates that BMI partially mediates the relationship between strategic agility and FP (β = 0.290; p < 0.001), supporting H4. These findings suggest that firms reporting higher levels of strategic agility also tend to report higher levels of FP, both directly and indirectly through higher levels of BMI. Furthermore, absorptive capacity significantly moderates the relationship between strategic agility and BMI (β = 0.076; p = 0.001), supporting H5. The results of the moderation analysis are presented in Figure 4.

The interaction between absorptive capacity and strategic agility on BMI produces an ƒ² value of 0.023, indicating a small moderating effect. This finding aligns with the slope plot (Figure 2), which demonstrates that the positive association between strategic agility and BMI becomes stronger at higher levels of absorptive capacity. Specifically, the slope for high absorptive capacity (+1 standard deviation [SD]) is steeper than the slopes for mean and low absorptive capacity (–1 SD), suggesting that firms reporting stronger absorptive capacity tend to show a stronger association between strategic agility and BMI. Although the effect size is small, the plot indicates that absorptive capacity plays an enabling role in strengthening the observed agility–innovation relationship.

The results of the control variable analysis are presented in Table 6.

The results of the structural model analysis for both models are summarised in Table 6. The comparison between the model without control variables and the model with control variables shows that the inclusion of demographic controls does not materially alter the direction, magnitude or statistical significance of the main hypothesised relationships. Although slight changes in coefficient values were observed, the major structural relationships remained statistically significant and consistent across both model specifications. Among the control variables, only age showed a significant relationship with FP (β = −0.098, p = 0.004), suggesting that older respondents tended to report slightly lower evaluations of FP. In contrast, management tenure, work experience, educational level and gender did not have significant effects on FP. These results provide additional support for the robustness of the proposed model, as the main statistical relationships remain stable after controlling for respondents’ demographic characteristics.

This study offers several theoretical implications for the dynamic capability literature by explaining how strategic agility, BMI and absorptive capacity operate as interconnected mechanisms in state-owned logistics enterprises. The theoretical contribution lies not merely in confirming the relationships among these constructs but in clarifying the mechanisms and boundary conditions under which dynamic capabilities become performance-relevant in an institutionally constrained context. Firstly, this study refines the performance logic of strategic agility by showing that its value is context dependent rather than universal. From the dynamic capability perspective, agility becomes performance-relevant when sensing and seizing are followed by effective reconfiguration through resource realignment, cross-unit coordination and strategic process integration. In state-owned logistics enterprises, this means that agility should not be viewed merely as rapid responsiveness, but as a capability whose value depends on whether adaptive responses are embedded within organisational systems that allow them to become meaningful strategic action. Secondly, this study contributes to the literature by positioning BMI as a value-conversion mechanism linking strategic agility and FP. Strategic agility reflects sensing and seizing, while BMI reflects reconfiguration through the renewal of value creation, value delivery and value capture mechanisms. This clarifies why agility alone may not be sufficient to generate performance outcomes. Adaptive responses may remain temporary, operational or administrative unless they are converted into coherent business model renewal. Thirdly, this study sharpens the theoretical role of absorptive capacity by conceptualising it as a boundary condition rather than merely as an antecedent or mediator. The theoretical novelty lies in explaining absorptive capacity as a knowledge-processing condition that determines whether strategic agility is converted into informed, relevant and implementable BMI. Strategic agility may enable firms to respond quickly to environmental change, but absorptive capacity helps determine whether these responses are aligned with business model renewal. This insight helps explain why agility may generate different innovation outcomes across organisations, particularly in SOEs, where external knowledge must pass through bureaucratic routines and constrained managerial discretion before becoming a business model renewal. Fourthly, this study clarifies the relevance of both potential and realised absorptive capacity to the agility–BMI relationship. Potential absorptive capacity enables firms to acquire and interpret external knowledge, while realised absorptive capacity enables firms to transform and apply that knowledge into business model renewal. This distinction is important in state-owned logistics enterprises because external knowledge may be available, but bureaucratic routines and limited managerial autonomy may hinder its transformation into new services, delivery systems, digital platforms or customer engagement mechanisms. Fifthly, this study extends dynamic capability theory by highlighting the SOE context as an institutional boundary condition. Much of the dynamic capability literature assumes that firms can reconfigure resources after sensing and seizing opportunities. However, in SOEs, strategic action is shaped by public accountability, regulatory oversight, hierarchical control, risk aversion and public service obligations. Therefore, the effectiveness of dynamic capabilities in SOEs depends not only on firms’ ability to sense, seize and reconfigure resources but also on institutional and organisational constraints, including bureaucracy, regulatory control and limited managerial autonomy. Taken together, these implications show that dynamic capabilities should be understood as interdependent rather than isolated mechanisms. Strategic agility provides responsiveness, BMI provides the reconfiguration mechanism, and absorptive capacity provides the knowledge-processing condition that strengthens the conversion of agility into business model renewal. This integrated view advances dynamic capability theory by explaining how adaptive capabilities operate within state-owned logistics enterprises, where strategic renewal depends not only on capability possession but also on institutional conditions and organisational readiness.

This study offers practical implications for leaders and policymakers in state-owned logistics enterprises undergoing digital transformation. The findings suggest that strategic agility, BMI and absorptive capacity should be managed as interconnected capabilities rather than as separate managerial initiatives. To make these capabilities operational, organisations need clear interventions, responsible units and measurable managerial outcomes. Firstly, strategic agility should be institutionalised through stronger cross-unit coordination and faster resource reallocation. Top management, strategy units, regional operations units and human capital divisions should establish cross-functional response teams supported by regular coordination forums, escalation procedures and decision dashboards. These mechanisms can help organisations monitor customer demand, digital logistics trends, service complaints and operational bottlenecks more effectively. Managers can monitor progress through shorter decision cycles, faster approval of service improvement initiatives, reduced coordination delays, improved delivery reliability and faster complaint resolution. Secondly, strategic agility should be translated into BMI rather than remaining limited to operational adjustment. Business development units, digital transformation teams, operations divisions and commercial units should jointly redesign value propositions, service delivery systems, customer interfaces and revenue mechanisms. In state-owned logistics enterprises, this may include developing digital tracking services, improving last-mile delivery models, strengthening e-commerce logistics partnerships, introducing customer-oriented service packages and redesigning pricing or cost structures for specific customer segments. Progress can be assessed through the number of new digital services launched, digital platform adoption, customer satisfaction, delivery timeliness, service reliability, e-commerce-related revenue growth and operational productivity. Thirdly, absorptive capacity should be strengthened through structured knowledge-sharing and learning systems. Human capital divisions, knowledge management units, digital transformation teams and regional branches should develop cross-branch learning forums, customer insight repositories, post-project reviews, innovation workshops and pilot projects with technology partners. These mechanisms can help transform knowledge from customers, regulators, logistics partners, technology providers and frontline employees into service and business model improvements. Managers can monitor progress by tracking the number of customer insights converted into service improvements, the frequency of knowledge-sharing activities, employee participation in innovation programmes, the number of pilot projects implemented and the proportion of successful pilots scaled across branches. Finally, because absorptive capacity has a significant but relatively small moderating effect, managers should support learning capability by providing complementary organisational conditions. State-owned enterprise leaders and governance bodies should strengthen leadership commitment, digital infrastructure, resource availability, regulatory flexibility and managerial empowerment. Practical actions may include simplifying approval procedures for innovation pilots, allocating dedicated budgets for digital experimentation and giving regional units limited autonomy to test customer-oriented logistics solutions. Practical success should be assessed not only through financial outcomes but also through service reliability, delivery speed, customer responsiveness, digital service adoption and scalable business model renewal.

Although this study provides important insights into the relationships among strategic agility, BMI, absorptive capacity and FP, several limitations should be acknowledged. Firstly, this study employed a cross-sectional research design, which limits the ability to capture the dynamic evolution of these capabilities over time. As strategic agility, BMI and absorptive capacity may develop gradually across different stages of organisational transformation, future research should use longitudinal or panel data to examine how these capabilities interact and evolve over time. Such designs would also provide stronger evidence regarding the temporal ordering of the relationships examined in this study. Secondly, the study relied on self-reported survey data collected from managerial employees. Although managers are appropriate respondents because they possess relevant knowledge of strategic decisions, innovation activities and organisational performance, the use of a single-source survey may still raise concerns about common method bias and perceptual bias. This study applied procedural and statistical remedies to reduce this concern, but common method bias cannot be fully ruled out. Beyond common method bias, the cross-sectional design also raises concerns about reverse causality and endogeneity. The direction of association may operate in both ways, as firms with stronger prior performance may subsequently invest more in agility, business model renewal or absorptive capacity development, and unmeasured variables such as digital maturity, leadership cognition, governance quality or environmental turbulence may jointly shape both adaptive capabilities and FP. Future studies using longitudinal designs, time-lagged measurement or instrumental-variable approaches would help establish stronger temporal ordering and more rigorously address potential endogeneity. Future studies should consider using multi-source data, such as combining managerial surveys with objective performance indicators, archival data, customer evaluations or supervisor assessments to strengthen the validity of the findings. Thirdly, the study focused on a state-owned logistics enterprise within a single national context. While this context is theoretically relevant because state-owned logistics firms operate amid regulatory complexity, bureaucratic procedures, public service obligations and organisational rigidity, it may limit the generalisability of the findings. Future research could conduct comparative studies across ownership structures (state-owned, private and multinational), industries and countries. Such comparisons would help determine whether the relationships identified in this study are specific to state-owned logistics enterprises or applicable to broader organisational settings. Fourthly, absorptive capacity was examined as a unified moderating construct. However, prior literature distinguishes between potential absorptive capacity, which involves knowledge acquisition and assimilation, and realised absorptive capacity, which involves knowledge transformation and exploitation. Future studies could disaggregate these dimensions to examine whether potential and realised absorptive capacity play different roles in strengthening the relationship between strategic agility and BMI. This would provide a more detailed understanding of which learning processes are most important for converting strategic responsiveness into business model renewal. Finally, this study did not include several organisational and contextual variables that may shape the effectiveness of strategic agility, BMI and absorptive capacity. Future research could incorporate variables such as digital maturity, leadership cognition, organisational culture, governance mechanisms, regulatory flexibility and managerial autonomy. Including these factors would help clarify the boundary conditions under which adaptive capabilities are more likely to support BMI and FP.