External barriers to lean supply chain practices impacting firm performance: sensing and responding capability moderation

In contemporary markets, global competitiveness has shifted from individual firm performance to the collective strength of the entire supply chain (Garcia-Buendia et al., 2025). Moreover, supply chains are increasingly pressured to integrate sustainability considerations into their operations (Lyama and Salema, 2025). Meanwhile, global disruptions and regulatory mandates have exposed systemic vulnerabilities (López et al., 2025); thus, supply chains are increasingly becoming instruments of national policy and security. Manufacturers traditionally employ lean principles for optimizing internal efficiency, yet operational proficiency does not consistently ensure firm-wide success (Takcı et al., 2025). A critical structural gap persists, where internal mastery is frequently undermined by barriers – including leadership dynamics, cultural nuances and institutional rigidities – that remain beyond the organization's immediate control (Bhasin, 2012; Jadhav et al., 2014; Zhang et al., 2017). Consequently, lean supply chain management has emerged as a strategic priority for manufacturing firms, as it extends lean manufacturing beyond firm boundaries to orchestrate value flow from suppliers to end-users (Martínez-Jurado and Moyano-Fuentes, 2013; Khawka et al., 2025). Although managers are familiar with lean manufacturing principles within their internal operations, extending lean practices across the broader supply chain to improve firm performance remains challenging. Even when facilitated by strategic cooperation and digital integration, synchronized inter-organizational performance often remains elusive. Ultimately, firms struggle to translate internal lean proficiency into systemic outcomes due to persistent misalignments across complex organizational and institutional interfaces (Takeda-Berger et al., 2021; Zhang et al., 2017). Specifically, managers continue to face uncertainty over which external barriers they must overcome when attempting to deploy lean practices beyond their organizational boundaries. Although sensing and responding capabilities are recognized as vital forces enabling organizations to navigate an increasingly volatile global business environment (Teece, 2007; Nguyen et al., 2025), their buffering role against external lean supply chain barriers remains insufficiently understood among manufacturing firms in emerging economies like Vietnam.

Extant research has extensively examined lean supply chain dimensions and practices across firm and network levels utilizing diverse methodologies like including structural equation modeling (SEM), ISM-MICMAC and case studies (e.g. Abu et al., 2021; Ali et al., 2020; Narkhede et al., 2020; Pardi et al., 2024). While prior research has cataloged external lean supply chain barriers (e.g. Berger et al., 2018; Marodin et al., 2017; Takeda-Berger et al., 2021) and mapped the hierarchical structure of internal inhibitors (e.g. Ali et al., 2020; Narkhede et al., 2020), the dominant approach remains fundamentally static. Current frameworks frequently treat barriers as isolated obstacles rather than mutually reinforcing conditions within complex networks (e.g. Moyano-Fuentes et al., 2020). Consequently, this inward-looking logic overlooks a critical theoretical gap: the transmission mechanisms through which external shocks – such as partner unreliability or demand distortion – propagate inward to disrupt core just-in-time flows and force defensive inventory buffering (e.g. Cadden et al., 2020; Nguyen et al., 2017; Teo et al., 2018). Especially, explaining how external frictions spread inside to undermine a focal firm's core lean system is still a significant blind area (Marodin et al., 2016). Through Ozanne et al.’s (2022) lens, supply chain risks are inherently dynamic, uncertain and non-linear, rather than static, linear threats to performance. Furthermore, lean supply chain research remains geographically concentrated within specific emerging economies like India and China; existing studies in the Vietnam's context are largely descriptive or firm-centric, failing to address the systemic role of external environmental barriers (e.g. Le and Nguyen, 2021; Nguyen and Chinh, 2017).

To address these theoretical and contextual gaps, this study advances lean supply chain research by shifting attention from the identification of barriers to the capability-based mechanisms through which firms mitigate their adverse performance effects. Particularly, it investigates how sensing capability and responding capability buffer the negative impacts of external barriers, thereby strengthening overall firm performance. Accordingly, this study grounded in the extended resource-based view (ERBV) and dynamic capability theory (DCT) utilizes SEM to analyze survey data from 234 manufacturing firms in Vietnam. While ERBV accounts for how external lean supply chain research barriers arising outside firm boundaries constrain inter-organizational resource coordination, DCT complements this view by framing sensing and responding capabilities as the adaptive buffers needed to reconfigure resources and protect operations. Ultimately, this integrated RBV–DCT perspective provides a solid theoretical foundation for explaining how manufacturing firms sustain performance despite external constraints – a mechanism highly salient in Vietnam, where expanding integration into global production networks and deep partner dependencies heighten both shock exposure and the need for localized absorption capabilities.

This study is structured as follows. After the introduction, Section 2 reviews the foundational literature to develop the research hypotheses linking external lean supply chain barriers, dynamic capabilities and firm performance. Section 3 then outlines the research methodology, detailing the sample selection, data collection, construct operationalization and SEM procedures. Next, Section 4 presents the empirical results, discussing the findings relative to the hypothesized relationships before elaborating on their theoretical and managerial implications. Finally, Section 5 highlights key insights, acknowledging structural limitations, and offering avenues for future research.

The current research addresses barriers to lean supply chain (LSC) practices in four main aspects as below.

The first line of lean supply chain barriers focuses on translating lean principles into supply chain contexts, highlighting external obstacles to lean supply chain management like weak IT infrastructure (e.g. Berger et al., 2018; Pardi et al., 2024), limited partner trust and poor information sharing (e.g. Takeda-Berger et al., 2021), the absence of integrated technology systems and poor supplier relationship management (e.g. De Souza and Pidd, 2011; Chakraborty and Gonzalez, 2018; Manzouri et al., 2013), logistics coordination problems (e.g. DeSanctis et al., 2018) and power asymmetry across buyer–supplier interfaces (e.g. Marodin et al., 2017), cultural resistance and lack of vision across multi-tier networks (e.g. Qureshi et al., 2022), etc. These barriers originate beyond the focal firm's direct control, making them more difficult to overcome than internal issues (Manzouri et al., 2013). Notably, while this literature has made an important contribution by cataloging and classifying external lean supply chain barriers (e.g. Islam and Anis, 2018), its dominant focus remains descriptive. External barriers are often treated as separate obstacles rather than as mutually reinforcing conditions within multi-tier supply networks (e.g. Merli and Mosconi, 2019; Takeda-Berger et al., 2021). Furthermore, the dynamic interactions of external barriers remain underexplored. Consequently, the critical, unresolved issue is no longer whether external barriers matter, but rather how their combined, systemic effects hinder the extension of lean principles beyond organizational boundaries.

The second research stream maps fundamental internal problems that undermine lean supply chain practices, such as misperception of lean's strategic direction (e.g. Bhasin, 2012), lack of long-term leadership vision and inadequate knowledge and training on lean (e.g. Lai et al., 2020; Abu et al., 2021), lack lean awareness and skills (e.g. Alam and Daril, 2022) and unsupportive cultures (e.g. Abolhassani et al., 2016; Ali et al., 2020). Rather than treating these internal barriers as isolated obstacles, the body of work establishes that they operate within a hierarchical, interdependent structure across multi-layered contexts. Methodologically, prior research utilizes Fuzzy AHP-TOPSIS and ISM-DEMATEL-MICMAC to construct complex taxonomies that map these structural interdependencies (e.g. Belhadi et al., 2017; Narkhede et al., 2020). These causal frameworks typically position budgetary and technical constraints at the operational level, cultural and relational barriers at the network linkage level and strategic knowledge gaps at the primary driving level (e.g. Ali et al., 2020; Narkhede et al., 2020). However, this traditional, inward-looking logic is increasingly challenged by emerging empirical evidence of a reverse disruption pathway as supplier unreliability may disrupt just-in-time flow and create waiting waste (e.g. Nguyen et al., 2017; Cadden et al., 2020), while poor information sharing drives demand distortion and buffering inventories (Teo et al., 2018). Specifically, initial insights indicate that external shocks – such as partner unreliability or poor information sharing – directly fragment internal just-in-time flows and force defensive inventory buffering (e.g. Cadden et al., 2020; Teo et al., 2018). Although Marodin et al. (2016) highlighted the need to examine the lean supply chain risk interface, a critical theoretical gap remains regarding the systemic mechanisms through which these external barriers propagate inward to destabilize a focal firm's internal lean system.

The third study stream shifts attention to how unresolved lean supply chain obstacles erode focal firm's operational performance and competitiveness. Within supply networks, internal vulnerabilities are compounded by external pressures, including customer and market pressure (e.g. Panwar et al., 2015), relational mistrust (e.g. Berger et al., 2018), integration failures (e.g. DeSanctis et al., 2018; Khawka et al., 2025), etc. These forces collectively disrupt lean flows, inflate costs and diminish responsiveness. While lean supply chain success relies on balancing internal skills and network orchestration, literature has yet to adequately isolate how external barriers cascade down to depress firm performance. Specifically, although Ozanne et al. (2022) highlight the potential role of capabilities in addressing unresolved barriers, they leave underexplored the capability-based mechanisms through which these barriers can be mitigated. To bridge this gap, Aslam et al. (2018) and Yan et al. (2022) point to adaptive capabilities as critical tools for sensing disruptions, reconfiguring resources and shielding firm performance from external lean supply chain barriers.

The fourth study stream focuses on how lean supply chain barriers have changed in the digital and global age. Technological infrastructure, particularly for small and medium enterprises (e.g. Berger et al., 2018; Garcia-Buendia et al., 2023), legal or regulatory barriers and cross-national managerial differences (e.g. Takcı et al., 2025; DeSanctis et al., 2018) and high-level IT and organizational capabilities (e.g. Khawka et al., 2025), etc. as buffers to counteract these external pressures. Particularly, sensing and responding capabilities – the capacities to identify and react to environmental turbulence (Teece, 2007) – is a crucial but little-studied moderator.

Shortly, the current research in lean supply chain practices has not fully studied external barriers. Businesses may overcome internal inertia and maintain performance in the face of external unpredictability thanks to this missing piece, which motivates this study.

Integrating the ERBV and DCT, this study examines how external barriers – specifically supply chain partnership and information management – impact firm performance. It further investigates the moderating role of sensing and responding capability in these relationships, defining them as the firm's ability to navigate inter-organizational challenges (Figure 1).

In this study, firm performance is defined as the degree to which a manufacturing firm achieves its operational and market-related objectives – including cost efficiency, delivery reliability, quality and customer responsiveness – relative to its competitors (Flynn et al., 2010). It is the focal outcome through which the effects of external barriers and dynamic capabilities are evaluated.

Supply chain partnership barrier refers to the obstacle that hinder effective collaboration, trust and synchronization among supply chain members, including poor supplier commitment (Manzouri et al., 2013), a lack of collaboration and trust among partners (Berger et al., 2018), opportunistic behavior (Patil and Kant, 2014), insufficient external support (Belhadi et al., 2017) and differing cultural or linguistic values within the chain (Patil and Kant, 2014). Accordingly, poor supplier commitment often leads to fragmented process alignment and less responsiveness across the chain (Manzouri et al., 2013). This lack of dedication can occasionally be accompanied by poor collaboration and little information sharing, which makes it challenging to apply lean methods throughout the supply network (Martínez-Jurado and Moyano-Fuentes, 2013; Jadhav et al., 2014). Ineffective supplier-customer collaboration leads to disruption in material flow and information sharing (Manzouri et al., 2014). Such inadequate collaboration erodes partners' mutual trust and increases relational distance in the supply chain. Additionally, open communication and collaborative problem-solving are discouraged by a lack of trust (McIvor, 2001; Manzouri et al., 2013). Increasing mistrust can lead to opportunistic actions and conflicting cultural values, which further undermine collaboration and impede ongoing development (Manzouri et al., 2014; Berger et al., 2018; Patil and Kant, 2014). Consequently, these interconnected partnership barriers weaken supply chain integration and constrain lean implementation, and ultimately diminish firm performance.

Information management barrier is behavioral and structural obstacle that hinders the efficient use, integration and flow of information within supply chain networks (van Donk, 2008), including inadequate technology (Belhadi et al., 2017; van Donk, 2008), poor knowledge-sharing and security (Patil and Kant, 2014) and information sharing reluctance (Manzouri et al., 2013). Inadequate technological infrastructure and information systems hinder the effective gathering, processing and sharing of operational data (Jharkharia and Shankar, 2005; Belhadi et al., 2017; Tiwari and Tiwari, 2018; Zhang et al., 2017). Much information sharing also occurs informally rather than through formal channels, leaving few structured spaces for knowledge creation (Teo et al., 2018; Patil and Kant, 2014). Poorly standardized information is more vulnerable to breaches, which in turn intensifies partners' reluctance to share data (Núñez-Merino et al., 2020; Takeda-Berger et al., 2021). The organization's capacity to adapt, innovate and coordinate efficiently is thereby reduced, as mutual learning and collaborative problem-solving are undermined (Huo et al., 2020). The information management shortcomings directly compromise lean supply chain deployment and overall firm performance (Teo et al., 2018).

Sensing capability is a firm's ability to recognize, analyze and predict opportunities and risks arising from its external environment (Teece, 2007). For businesses looking to maintain competitiveness through responsiveness and innovation in today's VUCA market conditions, sensing capability has become increasingly important (Garrido et al., 2020). Strong sensing capability helps organizations identify supply chain disruptions, technological developments and faster adjustment to market uncertainty (Laaksonen and Peltoniemi, 2018; Wamba et al., 2020). Consequently, sensing capability strengthens a firm's ability to mitigate disruptions stemming from partnership barriers when implementing lean supply chain practices, ultimately enhancing performance outcomes.

Information management barrier, arising from technological incompatibility, procedural deficiencies and reluctance to share information, weaken supply chain visibility and disrupt the synchronization required for lean operations (Manzouri et al., 2013; van Donk, 2008), … Although digital technologies like big data analytics, Internet of Things devices and enterprise resource planning systems enhance firms' ability to capture and integrate supply chain data, they also intensify challenges related to data quality, security and fragmented ICT adoption across partners (Ali et al., 2020; Wamba et al., 2020). These information management obstacles frequently impede the adoption of lean supply chain management, delaying decision-making and preventing lean operations from being smoothly synchronized across companies (Merli and Mosconi, 2019). Businesses with strong sensing capability keeps a close eye on data patterns and technology trends, which helps them spot discrepancies, foresee information bottlenecks and take remedial action to improve data quality and transparency throughout their supply chains (Garrido et al., 2020; Teece, 2007). As a result, sensing capability can buffer the negative effect of information management barriers on firm performance by improving information transparency and enabling timely corrective actions.

Supply chain partnership obstacles, such as purpose misalignment and a lack of trust, impede the flow of information and materials in unstable situations, which eventually deteriorates business performance (Shin et al., 2019). However, by rearranging competencies to adjust to changes in the environment, enterprises might lessen these detrimental effects, according to DCT (Teece et al., 1997). In particular, high responsiveness (which includes detecting, seizing and reconfiguring) enables businesses to recognize threats early and reorganize alliances to resolve competing interests (Teece, 2007; Garrido et al., 2020). By utilizing these capacities, businesses can turn collaborative conflicts into chances for innovation and performance improvements rather than just absorbing shocks (Ozanne et al., 2022). As a result, response capacity acts as a crucial moderator that may lessen the negative impacts of barriers to collaboration on business success.

Information management barrier – characterized by poor data quality and system fragmentation – disrupt coordination and trigger operational inertia, such as the bullwhip effect (Teo et al., 2018; Alam and Daril, 2022). Drawing from the dynamic capability view, a firm's responding capability acts as a resilience mechanism that allows for the rapid interpretation and reconfiguration of operations despite these informational disturbances (Aslam et al., 2018). High responding capability enables firms to bypass information bottlenecks through agile decision loops and cross-functional communication, thereby buffering performance against data inefficiencies (Teng et al., 2022). Consequently, the negative impact of information management barrier is significantly attenuated in organizations with robust adaptive capacities, whereas firms with low responsiveness suffer more severe performance degradation due to delayed adaptation.

Drawing from previous empirical studies, the measurement scales for the constructs in the research model (Figure 1) are developed. The scales are modified and improved upon from prior research incorporated with the theoretical underpinnings of the extended resource-based approach and the dynamic capability.

Supply chain partnership barrier is operationalized through five items addressing supplier commitment (Manzouri et al., 2013), collaboration and trust concerns (Berger et al., 2018), opportunistic conduct and cultural differences among supply chain partners (Patil and Kant, 2014). Information management barrier is measured by five items encompassing inadequate information systems (Berger et al., 2018), mismatched information technology (van Donk, 2008), limited knowledge-sharing spaces and data security concerns (Patil and Kant, 2014) and reluctance to exchange information (Manzouri et al., 2013). Six items modified from Flynn et al. (2010) are used to evaluate firm performance. Sensing and responding capabilities are evaluated through five and eight elements, respectively, as outlined by Garrido et al. (2020).

In-depth interviews are carried out with six top managers to ensure conceptual clarity and contextual appropriateness within the Vietnamese industrial setting. After this qualitative validation, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) are used to analyze the refined scales' structure before the structural model is tested.

The five-point scale is considered to be more accessible to respondents to understand (Dawes, 2008), reducing ambiguity and possibly increasing response rates (Babakus and Boller, 1992). Thus, in this study, a 5-point Likert scale is used to measure observed variables, allowing respondents to indicate how strongly they agree or disagree with each statement. A score of 1 indicates strong disagreement; 2, disagreement; 3, neutrality; 4, agreement; and 5, strong agreement.

The questionnaire was created in English and organized into two sections. The cover letter outlined the study's purpose, provided response instructions and stated that responses would be used for research purposes only and were sought from the expertise experienced managers in supply chain management or lean manufacturing companies.

The questionnaire was translated into Vietnamese to accommodate local respondents. Supply chain management specialists cross-checked the English and Vietnamese versions to ensure consistency. To improve validity and reliability, six managers with a minimum of five years of supply chain management experience modified the updated questionnaire and conducted in-depth interviews. Managers of manufacturing firms pretested the questionnaire to make sure it was comprehensive and easy to use before it was used in the main study. Consequently, the final version was sent directly to manufacturing companies in Vietnam via Google forms and paper-based prints.

This study examines the effects of external barriers to lean supply chain practices – supply chain partnership barrier and information management barrier – on firm performance, and the moderating roles of sensing and responding capabilities. As all constructs are conceptualized at the firm level, the manufacturing firm is the unit of analysis, while middle- and senior-level managers serve as key informants due to their knowledge of production, operations and supply chain activities.

The sampling frame is selected from manufacturing firms operating in major industrial zones in Southern Vietnam, including the Vietnam–Singapore Industrial Park, Ho Chi Minh City Hi-Tech Park and Ho Chi Minh City Export Processing Zone Authority. After retaining firms with complete contact information, the final sampling frame included 2,367 firms. Data are collected through both paper-based and online questionnaires. After screening for completeness and validity, 234 useable responses are obtained. The final sample covers manufacturing firms with different ownership types, sizes and industry backgrounds, including private, state-owned and foreign-invested firms.

Data were collected from July to October 2025. After excluding incomplete responses, non-manufacturing firms and cases with highly similar response patterns, 234 valid questionnaires remained from the initial 294 responses. The sample includes mechanical firms (33.33%), textile/leather/garment and dyeing firms (32.05%), chemical firms (11.54%), agricultural, forestry, and aquatic products processors (11.11%), consumer goods firms (6.41%) and other industries (5.56%). Most firms employ fewer than 100 employees (75.64%), followed by 101–250 employees (17.95%), 251–500 employees (2.14%) and more than 500 employees (4.27%), with an average firm age of 8.8 years.

The sample size of 234 manufacturing companies is adequate for SEM, exceeding the recommended minimum of 200 observations for stable estimates (Hair et al., 2019; Kline, 2016) and aligning with empirical studies within operations and supply chain management research, where response rates are often modest (Baruch and Holtom, 2008).

Using data from 234 firms, SEM is employed to test the proposed model and hypotheses. Following the criteria outlined by Hair et al. (2018), the analysis proceeds as follows. First, EFA using principal axis factoring with Promax rotation, together with Cronbach's alpha, is conducted to assess constructs' unidimensionality and internal consistency. Second, CFA is conducted to validate the measurement model by evaluating model fit, convergent validity and discriminant validity. Third, SEM is performed using maximum likelihood estimation in SPSS 26 and AMOS 24. Model fit is assessed using Chi-square/df, RMSEA, TLI and CFI (Anderson and Gerbing, 1988).

Following validation of the measurement model, SEM is used to examine the proposed hypotheses between external barriers to lean supply chain practices and firm performance. The moderating impact of sensing and responding capability is further investigated using multi-group SEM analysis. Consistent with SEM methodological guidance, each moderator is separately divided into high- and low-level subgroups (Appendix A) based on the median split, enabling the structural paths to be compared across different subgroup conditions (Hair et al., 2017; Collier, 2020). Prior operations and supply chain studies have likewise applied median-based high–low grouping for multi-group analysis (e.g. Hautala-Kankaanpää, 2023; Nguyen and Mai, 2022). Moderation is tested by comparing constrained and unconstrained models via chi-square difference tests, where significance indicates cross-group path variation and thus supports moderation.

Preliminary reliability is examined using Cronbach's alpha coefficient and EFA with SPSS 26 software. Items with Cronbach's alpha below 0.7 and inter-item correlation coefficients lower than 0.3 are eliminated to maintain scale reliability and internal consistency (Hair et al., 2018). Accordingly, two items are eliminated, SENCAP3 from the sensing capability construct which captures environmental scanning with strategic foresight and RESCAP1 from the responding capability construct which reflects the capacity to create, adjust and redesign when necessary. Therefore, the final dataset retains 27 observed variables for subsequent analyses.

The CFA results indicate an acceptable overall model fit without further item deletion: Chi-square/df = 1.388; TLI = 0.973; CFI = 0.976; RMSEA = 0.041, which meets Hair et al.'s (2018) model fit criteria. Average variance extracted ranges from 0.648 to 0.820, exceeding the 0.50 threshold of Hair et al. (2018), confirming convergent validity. Inter-construct correlation ranges from −0.309 to 0.451, all below unity (Steenkamp and Van-Trijp, 1991), indicating the discriminant validity among the constructs. Composite reliabilities range from 0.901 to 0.965, all of which are greater than the recommended threshold of 0.70 (Hair et al., 2018), demonstrating construct's reliability (Appendix B).

To examine the hypothesized structural relationships, the model is estimated using covariance-based SEM with maximum likelihood estimation in AMOS 26. The model fit indices satisfy Hair et al.'s (2018) recommended thresholds, indicating adequate structural fit: chi-squared/df = 1.794; p = 0.000; TLI = 0.971; CFI = 0.975, and RMSEA = 0.058 (Appendix C).

The standardized path coefficients show that all hypotheses are supported at p-value <0.05 (Appendix D). Hypothesis H1 is supported by the finding that supply chain partnership barrier has a negative impact on firm performance (β = −0.316, p < 0.001). Similarly, information management barrier negatively affects firm performance (β = −0.283, p < 0.001), thus supporting hypothesis H2.

To examine whether sensing capability moderates the relationship between lean supply chain management barrier and firm performance, the sample is divided into high and low sensing capability groups using a median split of the sensing capability construct, yielding 117 firms in each group. Multi-group analysis is then conducted by constraining the paths from the two external barriers to firm performance to be equal across groups – supply chain partnership barrier (β_3a-A = β_3a-B) and information management barrier (β_3b-A = β_3b-B). In the partially constrained model, the equality constraint increases the degrees of freedom from 204 to 205 and raises the chi-square value, indicating a potential moderating effect (Appendix E). The chi-square difference test (Δχ²) between the unconstrained and partially constrained models has p-value of <0.05, suggesting that the relationships between external barriers to lean supply chain practices and firm performance differ significantly across high and low sensing capability groups (Appendix E; Appendix F). Specifically, for the supply chain partnership barrier and firm performance relationship, the difference is significant at p = 0.032, below the 0.05 threshold, thus supporting Hypothesis H3a. This negative path is considerably strong for firms with low sensing capability than for those with high sensing capability (Appendix F). Likewise, the moderating effect of sensing capability is also confirmed for the information management barrier and firm performance relationship, with a chi-square difference test yielding at p = 0.021, below the 0.05 significance threshold, supporting H3b. The negative effect changes substantially in the group of low sensing capability, but not in the group of high sensing capability (Appendix F).

The same procedure is applied to responding capability. The sample is divided into high and low capability groups using a median split, yielding 101 and 133 firms, respectively. The equality-constrained paths from supply chain partnership barrier (β_4a-A = β_4a-B) and information management barrier (β_4b-A = β_4b-B) to firm performance are compared against the unconstrained structural model. The chi-square differences (Δχ²) between the constrained and unconstrained models are statistically significant for both relationships (Appendix G; Appendix H). Responding capability significantly alters the relationship between supply chain partnership barrier and firm performance (p = 0.018), less than 0.05, supporting H4a. It has been shown that the higher the level of responding capability, the lower the impact of supply chain partnership barrier on firm performance. The hypothesis H4b has the p-value (p = 0.045), less than 0.05, supporting that responding capability significant change of relationship between information management barrier and firm performance (Appendix G; Appendix H). It has been demonstrated that when firms have better responding capability, the impact of responding capability on information management barrier and firm performance is stronger.

The finding that supply chain partnership barrier negatively affects firm performance supports H1 and reinforces prior evidence from Takeda-Berger et al. (2021), Berger et al. (2018), Jadhav et al. (2014) and Manzouri et al. (2013). Supply chain partnership barrier such as lack of trust, poor communication and misaligned objectives hinders collaboration and process integration across firms. In lean supply chain settings, such misalignment impedes waste reduction, synchronized production and continuous improvement, thereby translating partnership deficiencies into operational inefficiencies and performance loss. Therefore, the presence of supply chain partnership barrier negatively affects coordination and continuous improvement, leading to a decline in overall firm performance.

The moderation results provide further nuance. The finding of H3a indicates that the adverse effect of supply chain partnership barrier is stronger among firms with low sensing capability. This suggests that firms lacking the ability to detect early signs of misalignment, coordination failure or governance inefficiency are more exposed to the performance consequences of partnership-related barriers. The finding of H4a further shows that responding capability weakens this negative relationship. Firms with stronger responding capability can adjust governance routines, reconfigure collaboration practices and address partnership disruptions more decisively, thereby reducing the extent to which partnership barriers undermine firm performance.

Collectively, these findings reposition supply chain partnership barriers from simple collaboration problems to strategic vulnerabilities in lean supply chain implementation. Their performance impact depends not only on the existence of trust, alignment and communication, but also on firms' ability to sense emerging relational breakdowns and respond before these disruptions erode operational performance.

Accordingly, this finding shifts managerial attention from merely forming partnerships to actively governing, monitoring and realigning them over time. Businesses should place a high priority on establishing partnerships based on justice, openness and trust in order to get over collaboration obstacles. Trust cannot rely in verbal commitments alone; it also needs to be reaffirmed by consistent behavior, competence and goodwill. Companies should proactively evaluate the reputation, skills and past achievements of their rivals before establishing strategic alliances to ensure alignment in values and strategic direction. During collaboration, goals, expectations and shared visions should be translated into explicit and measurable agreements that clarify accountability, fair benefit distribution and mutually owned outcomes. Disciplined collaborative decision-making, frequent feedback channels and the proactive establishment of psychological safety – which enables both parties to discuss, modify or settle disputes in an open and constructive manner – are all ways to preserve transparency. In order to assure that the value generated by lean projects is dispersed equitably and to strengthen trust and long-term cooperation, businesses should also design fair benefit-sharing and incentive systems. To maintain strong connections in unstable settings, partners must build dynamic coordination capabilities, leveraging Industry 4.0 technologies – such as automated decision-making, predictive analytics and integrated data platforms – to recognize environmental changes, realign goals and reorganize collaboration in real time. These digital facilitators turn collaborations into proactive, data-driven processes rather than reactive agreements. Long-lasting partnerships depend on choosing partners with aligned objectives, visions, missions and operational standards. Lacking this alignment, businesses must proactively educate themselves, adapt and integrate into supply chain ecosystems sharing these strategic orientations and values.

The significant negative effect of information management barrier on firm performance, which supports H2, is consistent with Takeda-Berger et al. (2021), Jadhav et al. (2014) and Jharkharia and Shankar (2005). In lean supply chain management, information flow is not merely an operational support mechanism but a prerequisite for synchronization, waste elimination and timely decision-making. Poor data sharing, system incompatibility and unreliable communication undermine visibility across the supply chain, exacerbate delays and inventory imbalances and weaken operational responsiveness. Consequently, information management barriers undermine lean execution and reduce overall firm performance. The moderation findings for H3b and H4b indicate that dynamic capabilities shape how firms experience these information-related constraints. H3b shows that the negative information management barrier–performance relationship changes substantially among firms with low sensing capability, suggesting that these firms are more vulnerable to data fragmentation, system incompatibility and delayed signals from supply chain partners. H4b demonstrates that responding capability also moderates this relationship. Firms with stronger responding capability are better able to mobilize resources, redesign processes and implement corrective actions, thereby buffering the performance impact of information management barriers. Thus, the performance impact of information management barriers depends not only on information disruption itself, but on firms' ability to sense informational breakdowns and respond before they compromise lean coordination and decision quality.

Managerially, information management should be viewed not as a back-office technical function, but as a strategic infrastructure for sustaining lean coordination and decision responsiveness. Companies should institute cross-functional data governance with distinct owners and consolidate key documents and transactions into a single source of truth to surmount information management obstacles. Standardizing partner interfaces (GS1/EDI/API) and shared data guarantees semantic consistency throughout its lifecycle. Given that communications span disparate channels like email, chat, forms and shared documents, an enterprise information-intake funnel must accommodate diverse inputs while automatically ingesting, de-duplicating, classifying and entity-linking each item to a canonical record to yield a single, integrated thread. Following that, implement event-driven ERP, MES, WMS and TMS integration; gather data at the source using barcodes, RFID, and IoT; and expose demand, inventory and schedules via e-kanban and a shared control tower. These steps will reduce the lead time of information to almost real time. Simultaneously, the operational model needs to provide just-in-time information, as suggested by Green et al. (2014): the minimal, precise signal that is tied to the canonical object and surfaced at the decision point at the final responsible moment – neither earlier (noise) nor later (rework). Furthermore, within predefined parameters, ML-driven system automatically executes low-risk decisions while triggering stakeholders' real-time notifications, streamlining information flows, detecting anomalies and predicting SLA violations. Role-based access, zero-trust and human-in-the-loop approvals strike a compromise between control and speed. Lastly, as conditions change, these automated signals are kept in line with planning and replenishment through institutionalized S&OP/S&OE and pilot CPFR, with dashboards monitoring data freshness and decision latency.

Overall, both sensing and responding capabilities serve as critical moderators that alter the relationship between external barriers to lean supply chain practices and firm performance. Rather than functioning as reactive operational tools, these dynamic capabilities enable firms to actively interpret, prioritize and address supply chain partnership and information management constraints through timely governance, coordination and information-system adjustments. This suggests that firms in Vietnam are transitioning beyond mere environmental awareness toward the strategic mobilization of internal mechanisms that simultaneously reduce external barriers and strengthen the organizational capacity to mitigate their adverse performance consequences.

This study bridges long-standing gaps in the literature on lean supply chain implementation by examining the impact of key external barriers – supply chain partnership and information management – on firm performance. It also examines the moderating effects of sensing capability and responding capability in these relationships. Using survey data from 234 manufacturing firms in Vietnam and in-depth interviews with six experts, the study's findings – which are based on the theoretical frameworks of ERBV and dynamic capabilities – show that both barriers significantly hinder firm performance, with supply chain partnership barrier having a more noticeable impact. We can expand on this by deepening our comprehension of how sensing and responding capabilities function as a dynamic capability that converts uncertainty in the environment into timely and coordinated organizational responses, in line with the study by Wamba et al. (2020) to proactively allocate available resources and strategic alignment.

Notably, this study advances lean supply chain literature by reframing external barriers. While prior research focuses on how internal barriers affect a firm's readiness for lean manufacturing, this study shifts the analytical lens to external constraints across the broader supply chain. Furthermore, whereas existing literature treats these obstacles as static and isolated, this study conceptualizes them as dynamic constraints that actively transmit disruptions into focal firms. Focusing on supply chain partnership and information management barriers, the empirical findings confirm that these external lean supply chain barriers carry direct performance implications. Ultimately, even internally optimized firms can suffer severe performance disruptions if they fail to manage these external headwinds. The primary theoretical contribution, however, lies in integrating sensing and responding capabilities. By uncovering the specific capability conditions that amplify or mitigate these adverse effects, this study shift the academic narrative from a descriptive catalog of what barriers exist to a predictive framework of how external barriers undermine performance. For manufacturers in Vietnam, this offers a capability-based architecture to interpret, prioritize and neutralize externally transmitted constraints during lean supply chain implementation.

Despite its contributions, this study possesses several boundary conditions that offer fruitful avenues for future inquiry. First, this study's empirical scope is intentionally restricted to the manufacturing sector, which may limit the generalizability of the findings to service-driven industries. Future research should extend this framework into environments where real-time responsiveness and flexibility are uniquely critical, such as healthcare and logistics. Second, while this quantitative approach successfully identified macro-level trends, it primarily captures linear relationships. Consequently, it may not fully capture the nuanced, non-linear interactions between organizational culture, institutional context and supply chain competencies. To address this, future scholars should employ simulation-based or mixed-method techniques – such as system dynamics – to model the longitudinal feedback loops between lean barriers, organizational capabilities and supply chain performance. Third, the further validation of these afore-mentioned managerial implications requires large-scale quantitative research to test the effectiveness, direction and magnitude of the proposed actions after implementation. Fourth, this conceptualization of dynamic capabilities focuses strictly on sensing and responding. While these are foundational, sustaining lean efficacy over time requires a broader suite of routines. Future studies should incorporate complementary capabilities such as organizational learning, coordination and asset reconfiguration to provide a more holistic view of long-term lean viability. Methodologically, because this study relies on a sample-median split to operationalize these capability levels, future work should aim to establish absolute, exogenous empirical thresholds to validate these cohorts. Finally, as global supply chains face unprecedented disruptions, there is an urgent theoretical need to connect operational efficiency with strategic viability. Future research should integrate dynamic capabilities with sustainability and resilience imperatives, laying the groundwork for a new theoretical framework of sustainable lean supply chains in highly volatile global environments.

We would like to express our gratitude to the supply chain experts for their valuable and insightful comments in in-depth interviews and the managers of manufacturing firms in Vietnam for their participation in the survey

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