Information Sharing, Quality Management, and Firm Performance: The Mediating Role of Supply Chain Agility

Abstract

The fashion industry’s business is becoming increasingly complicated and active. This industry is expected to be highly competitive, particularly in the retail sector. Therefore, this research aims to examine the impact of supply chain information sharing and quality management on firm performance, with supply chain agility as a mediating variable, in the Asian fashion industry. A total of 169 participants from the fashion sector in a developing country were surveyed. The proposed hypotheses were examined using a quantitative approach, employing Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS to assess and validate the measurement model. The results indicate that supply chain information sharing and quality management have a significant impact on a firm’s performance. Similarly, the sharing of supply chain information and quality management has a significant impact on firm performance by mediating supply chain agility. The study offers actionable insights for managers in volatile fashion supply chains. Firms can enhance performance by sharing real-time demand and inventory information, strengthening key quality practices, and adopting flexible, data-driven production processes. Integrating information sharing, quality management, and agility enables faster responses to shifting consumer trends, thereby improving overall competitiveness in fast-fashion environments. This study offers valuable guidance for supply chain professionals seeking to enhance practices within their networks. The results underscore the strategic importance of information sharing and quality management in promoting agility, an essential capability for achieving a competitive advantage. Additionally, the insights generated are relevant to practitioners, policymakers, and industry leaders aiming to strengthen supply chain responsiveness and resilience.

1. Introduction

The fashion industry has evolved into a highly dynamic and competitive sector, particularly within retail, where short product life cycles and rapidly shifting consumer preferences intensify operational pressures. As a cross-sector domain that spans apparel, brands, and trade channels, its complexity has heightened the need for robust supply chain capabilities [1]. In this environment, supply chain agility has emerged as a critical capability, enabling firms to sense and respond to market fluctuations, reconfigure resources, and manage disruptions effectively. With global competition intensifying, organizations are adopting advanced supply chain practices, such as information sharing and quality management, to enhance responsiveness and mitigate risk. Information integration helps coordinate activities across partners and strengthens competitive positioning [2], while quality management supports customer retention, operational efficiency, and financial performance [3].

Despite these developments, the fashion supply chain remains highly uncertain and irregular, with customer demand fluctuating frequently and unpredictably. Firms in turbulent markets such as Karachi, Pakistan, face unique challenges, including infrastructural constraints and inconsistent supply conditions, prompting them to adopt information-sharing practices to mitigate operational risks [4,5]. Although prior studies acknowledge that information sharing and quality management contribute to agility and performance [6,7], existing research has not jointly examined these two practices as parallel antecedents of supply chain agility and firm performance within the Asian fashion industry, nor has it evaluated how agility mediates their effects in a high-volatility market. Studies have explored portions of this relationship, such as information sharing and agility [8], information visibility and collaboration [9], or information quality and performance [10], but a comprehensive model integrating information sharing, quality management, supply chain agility, and firm performance remains empirically underdeveloped, particularly in fashion markets of developing economies.

The novelty of this study lies in its integrated examination of information sharing and quality management as simultaneous antecedents of supply chain agility and firm performance within the Asian fashion industry, a context characterized by volatility, short product life cycles, and limited empirical attention. While prior studies have investigated information sharing in relation to operational performance or explored quality management in isolation, very few have jointly tested these constructs alongside supply chain agility as a mediating mechanism in a single comprehensive model. Existing research [1,11,12] provides partial insights but does not address how the combined effects of information sharing and quality management translate into agility-driven performance outcomes, particularly in developing economies such as Pakistan. This gap motivated the present study, which extends the literature by empirically validating a holistic framework that explains how firms can enhance competitiveness through the interplay of information integration, quality practices, and agility capabilities in a highly dynamic market. Accordingly, this study aims to address these gaps by pursuing the following objectives:

RO1: 

To examine the direct effects of information sharing (IS), quality management (QM), and supply chain agility (SCA) on firm performance (FP).

RO2: 

To assess the effect of SCA on FP, and to investigate whether SCA mediates the relationships between IS, QM, and FP in the fashion retail sector of Pakistan.

By doing so, the study provides a consolidated understanding of how firms can leverage operational practices and agility capabilities to enhance competitiveness in a rapidly changing fashion industry. Considering the problem and research objectives, the following research questions aim to offer thorough insights.

RQ1: 

To what extent does IS affect SCA?

RQ2: 

To what extent does IS affect FP?

RQ3: 

To what extent does QM affect SCA?

RQ4: 

To what extent does QM affect FP?

RQ5: 

To what extent does SCA affect FP?

RQ6: 

To what extent does SCA mediate the relationship between IS and FP?

RQ7: 

To what extent does SC agility mediate the relationship between QM and FP?

The following sections of this research are structured as follows: Section 2 comprises a literature review drawing on prior studies. Section 3 outlines the methodology, while Section 4 covers the analysis and testing of both direct and indirect hypotheses using data. Section 5 concludes with discussions, research implications, and recommendations.

2. Literature Review

2.1. Theoretical Background

This study draws on information processing theory (IPT), quality management theory (QMT), and the resource-based view (RBV) to develop an integrated explanation of how information sharing and quality management jointly enhance supply chain agility and firm performance, particularly within the highly volatile fashion sector. IPT posits that organizations operating under high uncertainty must expand their information-processing capacity through relational mechanisms such as real-time information exchange and inter-organizational coordination [13,14]. Fashion supply chains, characterized by short product life cycles, rapid trend turnover, and unpredictable demand patterns, require firms to acquire, interpret, and disseminate information far more rapidly than those in stable industries [15]. Within this context, information sharing becomes a strategic necessity, enabling firms to reduce ambiguity, synchronize decisions with partners, and respond more effectively to shifting consumer preferences [5,16].

QMT complements this logic by emphasizing that effective quality management practices establish the organizational discipline, process stability, and continuous improvement routines necessary to operationalize shared information [17,18]. Foundational work by Garvin (1983) highlighted how structured quality practices enhance consistency and reduce process variation, while contemporary studies show that QM strengthens supply chain performance, innovativeness, and ERP implementation success [19,20]. In fast-moving fashion supply chains, such routines ensure that rapidly exchanged information translates into reliable execution, faster design cycles, and higher responsiveness, attributes essential in markets where production and replenishment windows are extremely narrow.

RBV provides the overarching conceptual lens connecting these constructs by arguing that organizations gain sustained competitive advantage when they accumulate and deploy valuable, rare, inimitable, and non-substitutable resources [6,21]. Information sharing and quality management can be viewed as strategic resources that jointly enable the development of supply chain agility, a dynamic capability that allows firms to sense market shifts and respond with speed and flexibility [22,23]. Prior research indicates that agility enables faster market responses, improved customer satisfaction, and enhanced operational performance across various industries [1,24]. In fashion, these advantages are amplified because misalignment between supply and demand immediately leads to lost revenue or excess inventory.

By integrating IPT, QMT, and RBV into a unified narrative, this study makes a conceptual contribution beyond testing known relationships in a new context. It highlights that in industries with extreme demand volatility and short life cycles, such as fashion, information sharing and quality management are not independent operational practices but interdependent resources that jointly build agility capabilities. This distinguishes fashion from more stable sectors, where these practices may improve performance independently without requiring agility as a mediating mechanism. Thus, the study advances theory by explaining why the joint examination of IS and QM through agility is uniquely relevant to fashion supply chains and by positioning agility as the dynamic capability through which these resources translate into competitive performance.

2.2. Empirical Reviews

2.2.1. Information Sharing (IS)

Prior studies on information sharing and supply chain performance offer valuable insights, yet they also reveal inconsistencies and unresolved questions that justify further investigation. Vafaei-Zadeh et al. (2020) demonstrated that information quality, security, and technology significantly enhance supply chain information integration, but only intentional, not accidental, information leakage moderates the relationship with performance [25]. This suggests that information does not uniformly improve performance; rather, its effects depend on the context and purpose for which it is shared. Similar nuances are evident in recent work, which shows that information capabilities do not always translate into performance unless supported by collaborative structures and technological readiness [26,27]. Likewise, Gu et al. (2020) found that exploratory rather than exploitative IT usage plays a more critical role in building supplier and customer resilience, revealing divergent pathways through which information-related capabilities may influence outcomes [28]. These differences indicate that information mechanisms may not consistently translate into performance benefits across supply chain settings.

Other studies highlight additional complexities. Bogetoft and Kromann (2018) observed that firms using electronic information sharing outperform matched firms that do not; however, their analysis also reveals significant heterogeneity across firms, suggesting that information sharing alone is insufficient unless complemented by other organizational capabilities [29]. This aligns with studies showing that information-based practices often require complementary integrative or technological capabilities to generate meaningful performance outcomes [30]. Experimental work by Srivathsan and Kamath (2017) shows that increasing information sharing levels can reduce stockouts and improve system performance, but only up to a point, beyond which diminishing returns set in [31]. Research in healthcare supply chains further underscores this tension: Kochan et al. (2017) demonstrated that cloud-based information sharing enhances visibility and reduces shortages; however, this improvement depends on hospitals’ ability to interpret and act on shared information, a capability that varies widely across institutions [32].

Taken together, these studies reveal a recurring pattern: information sharing improves supply chain outcomes, but its impact is neither uniform nor automatic. Performance gains materialize only when firms possess complementary capabilities, such as resilience, technological ambidexterity, or operational responsiveness, that allow them to convert information into timely and effective action. Recent research supports this capability-based interpretation, showing that information does not create value unless firms can absorb, process, and operationalize it through dynamic or integrative mechanisms [33,34]. This observation highlights a key tension in the literature: while information sharing is often portrayed as a performance enhancer, empirical evidence suggests that information alone does not guarantee improved performance unless firms can rapidly sense, interpret, and respond to market shifts.

This tension directly motivates the current study. Despite extensive research on information sharing, very few studies have examined how information translates into firm performance in fast-paced industries, such as fashion, where agility, rather than resilience or visibility, may serve as the critical conversion mechanism. This finding is consistent with emerging scholarship that emphasizes agility as a central capability for transforming information flows into a competitive advantage in turbulent markets [33,35]. Existing research seldom addresses the joint influence of information sharing and quality management, nor does it fully explain whether supply chain agility mediates their impact, an important gap given that agility is uniquely relevant in sectors characterized by short product life cycles, high demand volatility, and constant trend changes.

2.2.2. Quality Management (QM)

Research on quality management (QM) within supply chains provides important insights, but it also reveals several tensions and unresolved issues that underscore the need for further investigation. Zaid and Baig (2020) observed that existing supply chain quality management literature insufficiently integrates both innovation and operational performance, despite both being central to firm competitiveness [36]. Their work highlights the underexplored roles of QM competencies and knowledge transfer (KT) as foundational drivers of QM effectiveness, aligning with recent findings that show knowledge-related mechanisms mediate the impact of internal capabilities on firm performance [23]. However, while Zaid and Baig’s conceptual model demonstrates interconnected relationships among QM practices, QM skills, and KT, it remains unclear how these capabilities interact with broader supply chain mechanisms to influence firm performance under volatile conditions, a limitation particularly relevant for fast-changing sectors such as fashion.

Empirical evidence from Zhou and Li (2020) reinforces this inconsistency. Although their study found that QM and supply chain strategies significantly enhance market and innovation performance in Chinese SMEs, it also shows substantial regional and competitive variations [11]. Firms in highly competitive environments were less willing to invest in supplier-specific initiatives, and QM benefits varied across geographies. This variation suggests that QM effects are sensitive to competitive intensity and contextual differences, complicating assumptions about their universal impact on performance. Hong et al. (2019) provide further nuance by demonstrating that QM practices indirectly influence operational performance through innovation capabilities, while exerting no direct operational impact [37]. This contradicts classical QM perspectives that assume a direct link between quality practices and operational improvement. Their findings align with emerging research indicating that internal capabilities often influence performance indirectly through dynamic supply chain mechanisms [34]. Such contingency invites further exploration of intermediary capabilities, such as agility, that convert QM initiatives into performance gains in time-sensitive industries.

The literature also shows conflicting interpretations regarding the compatibility of QM with flexibility and responsiveness. Qamar et al. (2019) found an inverse relationship between quality and flexibility in the automotive sector, challenging the assumption that high-quality processes automatically promote adaptability [38]. Their findings revealed that lean and agile business models generate distinct performance outcomes, suggesting that QM alone cannot ensure responsiveness in dynamic environments. This tension parallels contemporary insights that firms require complementary routines, including collaboration, integration, and technological enablement, to translate quality initiatives into agile responses [39]. Studies linking QM with organizational culture and adaptability provide partial answers. Panuwatwanich and Nguyen (2017) demonstrated that TQM enhances performance only when aligned with supportive cultural dimensions; in hostile cultural or market conditions, its effects weaken [40]. Similarly, Abuzaid (2015) empirically showed that QM enhances strategic agility in hospitals, with customer orientation and supplier management being the most influential QM dimensions, reinforcing the idea that certain quality practices are more agility-enabling than others [41].

Research by Pantouvakis and Bouranta (2015) further complicates the picture by showing that agility moderates, rather than mediates, the relationship between learning culture and customer relationship quality [42]. This indicates that agility may operate differently across contexts, sometimes amplifying the impact of organizational factors rather than serving as a direct pathway to performance. In parallel, Pereira-Moliner et al. (2012) demonstrated that QM stimulates other capabilities, such as environmental management, which, in turn, influence performance [43]. These patterns resemble multi-capability chains observed in recent SCM studies, where QM strengthens other internal or external capabilities (e.g., green functions, digital readiness, resilience mechanisms) that ultimately shape performance [44].

While QM is widely recognized as beneficial, the mechanism through which it improves performance remains inconsistent, contingent, and context-dependent. Studies disagree on whether QM directly enhances operational performance, indirectly fosters innovation, or interacts with flexibility and cultural readiness. These contradictions are especially problematic in industries characterized by high uncertainty, such as fashion, where rapid responsiveness is essential, and the cost of misalignment is high. Despite extensive scholarship, existing research has not sufficiently examined how QM contributes to firm performance through a dynamic operational capability, such as supply chain agility, nor how QM interacts with other enablers, including information sharing, to support agility under short product life cycles and extreme demand volatility. This gap directly motivates the mediating role of agility in the present study.

2.2.3. Supply Chain Agility (SCA)

Research on supply chain agility (SCA) has grown substantially, yet important conceptual and empirical inconsistencies remain. Suifan et al. (2020), for example, demonstrated that agility mediates the relationship between information-sharing quality and long-term sustainability in humanitarian supply chains [12]. Their work illustrates that agility enables organizations to translate information into improved operational outcomes. However, because humanitarian supply chains operate under donor-driven mandates and unpredictable emergency conditions, it is unclear whether similar mechanisms apply within commercial fashion supply chains, where volatility arises from rapidly shifting consumer trends and short product life cycles. This gap highlights the need to examine agility within environments driven by competitive pressures rather than humanitarian imperatives, an argument reinforced by recent work showing that agility-based mechanisms can differ substantively across industrial contexts [35].

In the fashion domain, specifically, Mustafid et al. (2018) proposed a conceptual framework that shows how agile information systems enhance competitiveness by improving market sensitivity and consumer responsiveness [1]. While this framework emphasizes the importance of agility in fast-moving sectors, it remains largely diagnostic and does not empirically test how foundational practices, such as information sharing or quality management, contribute to the development of agility. Similar gaps appear in emerging studies, suggesting that agility often results from the interplay of multiple organizational practices, including information-processing routines, collaborative structures, and integrative mechanisms, rather than from a single operational factor [30]. Likewise, supplier innovation strengthens both information sharing and agility; however, their results vary depending on the sourcing strategies employed. Local sourcing enhances these effects, whereas global sourcing weakens them. These findings show that agility does not emerge uniformly but depends on contextual factors, suggesting that information sharing alone may be insufficient without complementary internal capabilities, an argument echoed in studies emphasizing that agility requires internal absorptive, integrative, or dynamic capabilities [45].

Other studies add further complexity. Tarafdar and Qrunfleh (2016) demonstrated that supply chain practices mediate the relationship between agile strategy and performance, and that the agility of information sharing moderates this mediation [46]. Their results reveal multilayered interactions among strategy, systems, and operational routines, highlighting the need to identify which organizational practices most effectively build agility in time-sensitive settings. At a broader strategic level, Gligor et al. (2016) argued that agility theory lacks a clear identification of firm-level antecedents, finding that while market orientation enhances agility, supply chain orientation is also necessary [47]. This suggests that agility is contingent upon alignment across strategic intent, information flows, and organizational routines, an alignment reflected in recent findings showing that agility often emerges from coordinated internal and external capabilities [33].

Further ambiguity is evident in Gligor et al. (2015), who demonstrated that agility improves both cost efficiency and customer effectiveness but noted that its performance impact varies across environmental conditions, such as complexity and resource availability [48]. This variation suggests that agility may not consistently translate into improved performance unless it is supported by additional organizational capabilities. These patterns align with recent evidence indicating that agility enhances performance only when combined with complementary digital, integrative, or collaborative capabilities [44].

Collectively, these studies reveal recurring tensions: the antecedents of agility are inconsistently defined across sectors; information sharing is beneficial but not universally sufficient; and the performance effects of agility appear contingent and context-dependent. Although prior studies acknowledge agility’s mediating potential, very limited research empirically examines how information sharing and quality management jointly contribute to agility, particularly within fashion supply chains characterized by extreme demand volatility, compressed product cycles, and intense competitive pressures. Existing empirical work rarely tests agility as the mechanism through which these practices enhance performance in such rapidly changing environments. This gap provides the foundation for the present study, which investigates supply chain agility as the mediating link between information sharing and quality management and examines its impact on firm performance in the Asian fashion industry.

2.2.4. Firm Performance (FP)

Research linking technological capabilities, agility, and firm performance has expanded over the past decade, yet significant inconsistencies persist regarding how and under what conditions agility translates technology-driven insights into performance outcomes. Ashrafi et al. (2019) demonstrated that business analytics (BA) capabilities enhance agility by improving information quality and innovation capability; however, their results also revealed that environmental turbulence weakens the effect of agility on performance [49]. This finding challenges the conventional assumption that agility uniformly benefits firms under uncertainty, suggesting that agility’s impact is contingent upon contextual and complementary organizational factors. Similar contingent effects have been documented in recent supply chain research, where digitalization, cloud adoption, and artificial intelligence contribute to resilience or performance only when paired with agile or responsive internal structures [33].

Similarly, research on Agile Manufacturing (AM) shows fragmented conclusions. Iqbal et al. (2018) found that while internal infrastructure and TQM positively influence AM in Pakistan’s garment export sector, neither TQM nor JIT directly improves operational performance [50]. Instead, performance improvements emerge only when AM mediates the relationships. Their findings reinforce a recurring pattern in the literature: operational practices require an agility mechanism to unlock performance gains, particularly in dynamic industries. These outcomes parallel emerging studies showing that operational practices such as integration, collaboration, and people involvement often contribute to firm performance indirectly through mechanisms that enhance agility rather than through direct effects [5].

IT capability research provides additional evidence that agility frequently functions as a necessary conversion mechanism. Liu et al. (2013) found that absorptive capacity and supply chain agility mediate the influence of IT skills on performance, indicating that technology investments alone do not generate outcomes unless firms possess the capacity to absorb, interpret, and apply new information [45]. Parallel work by DeGroote and Marx (2013) demonstrated that IT enhances the supply chain’s ability to sense and respond to market conditions by improving decision speed and coordination across partners [51]. Their findings confirm that agility substantially improves revenue, profitability, speed to market, and customer satisfaction, but only when IT-enabled information flows are complemented by organizational responsiveness. This aligns with contemporary research, which shows that IT and analytics capabilities contribute to supply chain performance primarily when bundled with integrative or flexible capabilities [33].

Across these studies, a consistent tension becomes evident: technological and operational capabilities improve performance only when organizations possess the agility to convert information into timely action. However, existing research still lacks clarity on which specific practices most effectively cultivate agility, especially in sectors where rapid demand shifts are the norm rather than the exception. The evidence suggests that agility functions as a mediator; however, prior studies have not examined how both information sharing and quality management jointly contribute to agility capabilities, nor how this mechanism operates in industries such as fashion, where compressed product life cycles and high volatility render agility particularly critical [1,30].

This underscores a clear research gap: although agility is repeatedly identified as a key intermediary capability, its mediating role has not been empirically tested in the context of simultaneous information sharing and quality management, particularly within fast-fashion supply chains in developing economies. Addressing this gap is essential for understanding how firms can transform operational practices into sustained performance advantages under conditions of extreme market dynamism, an issue further elevated by the growing digitalization and complexity of supply chains worldwide [52].

Figure 1 illustrates the conceptual model with the direction of the hypothesis. A conceptual framework is an interlinked set of ideas about how a particular function is shared among its parts. The framework serves as a source for understanding causal or correlational links across events, ideas, observations, concepts, knowledge, interpretations, and other experiential elements. The research framework consists of two independent variables, IS and QM, one mediator, SCA, and one dependent variable, FP.

Based on the research objectives, theoretical background, literature review, empirical reviews, and the conceptual model, the research hypotheses are given below:

H1: 

IS significantly influences the SCA.

H2: 

IS significantly influences the FP.

H3: 

QM significantly influence the SCA.

H4: 

QM significantly influences the FP.

H5: 

SCA significantly influences the FP.

H6: 

SCA significantly mediates the relationship between IS and FP.

H7: 

SCA significantly mediates the relationship between QM and FP.

3. Methodology

This investigation employed a deductive strategy combined with a quantitative methodology to gather information from the designated population. The quantitative-deductive method is a widely recognized approach that involves numerical data and employs hypothesis testing to substantiate a theory. This mode of explanatory research enables the researcher to produce credible insights, provided that all parameters are well-defined and valid [53]. It effectively expounds upon theoretical concepts alongside empirical discoveries [54]. As highlighted by Rashid et al. (2025), a causal research design permits hypothesis testing and the derivation of numerical outcomes. Consequently, this study adopted a causal methodology to examine the relationships among the variables [35].

3.1. Data Collection

In this study, the firm served as the unit of analysis, and data were collected from managerial-level respondents who possessed direct knowledge of supply chain operations. This approach is consistent with prior research, which shows that managers involved in supply chain decision-making are suitable informants for evaluating organizational practices and performance [55]. The sampling frame for Pakistan’s textile and apparel sector was not publicly available; therefore, we employed a purposive sampling strategy to target supply chain managers, executives, and specialists who were best positioned to provide accurate and relevant information. This non-probability approach is appropriate when respondents must meet specific knowledge-based criteria [56,57], and such expert-driven sampling aligns with similar empirical studies in supply chain management.

A total of 420 professionals were approached across various textile and apparel firms through industry networks, LinkedIn groups, and professional associations. Of these, 169 usable responses were obtained, yielding a response rate of approximately 40%. This rate is acceptable for manager-level surveys in operations and supply chain research. Following Hair et al. (2012) and Gefen et al. (2011), a minimum of 77 responses was required, given the study’s model complexity, desired power (0.80), and the presence of three predictors [58,59]. The final sample size of 169, therefore, exceeds the recommended threshold, thereby strengthening the analysis’s statistical power.

A single knowledgeable respondent was selected per firm based on their involvement in supply chain planning, quality management, logistics, or coordination activities. This approach is widely accepted in SCM research because such individuals have access to cross-functional insights and can accurately represent organizational practices [57]. To ensure instrument clarity and contextual fit, the survey was pre-tested with ten supply chain professionals, after which refinements were made. Data were collected through an online questionnaire distributed from February to July 2023, with assurances of confidentiality and voluntary participation. Prior to analysis, the dataset was screened for completeness and response quality. Questionnaires with substantial missing data were excluded during the initial data cleaning stage. The final dataset contained no missing values because the online survey instrument used a mandatory-response design. Outliers were examined using standardized scores and Mahalanobis distance, and no extreme multivariate outliers were detected. Additionally, response quality was assessed by checking for straight-lining patterns and unusually short completion times; no problematic responses were identified. These procedures ensured the reliability and integrity of the dataset prior to conducting the PLS-SEM analysis.

3.2. Measurement Instrument

The instrument selected for this study was a questionnaire, as it supports the collection of numerical data. The tool employed in this study consisted of structured questions concerning the research variables designed for application with a relatively extensive participant pool [57]. The survey employed a five-point Likert scale, ranging from “strongly disagree” (1) to “strongly agree” (5). As these authors suggest, using pre-existing instruments is a plausible approach, either by adapting them to new elements or by employing them directly. This study incorporated existing items, as they had undergone prior review and validation. In this study, the IS has five measures adapted from Zhou and Li (2020) [11] and Sezen (2008) [60], and QM has five measures adapted from Zhou and Li (2020) [11] and Pereira-Moliner et al. (2021) [43]. Similarly, SCA and firm performance include five and four measures (respectively) from Gligor and Holcomb (2012) [61].

To ensure contextual relevance, minor wording adjustments were made to the original measurement items while preserving their conceptual meaning. Specifically, generic organizational references in the original scales were adapted to reflect the operational context of the textile and apparel supply chain industry in Pakistan. These adjustments were limited to terminology clarification (e.g., replacing general terms such as “organization” with “firm” or “company”) and aligning the wording with supply chain practices relevant to the study context. No substantive changes were made to the items’ conceptual content. All constructs were modeled as reflective, consistent with the theoretical definitions and prior empirical studies from which the scales were adapted. Reflective specification is appropriate because the indicators represent manifestations of the underlying latent constructs, and changes in the latent variable are expected to cause variations in the observed measures [34]. The reliability and validity assessments reported in the measurement model (factor loadings, composite reliability, AVE, and HTMT ratios) further confirm the adequacy of the adapted scales.

4. Data Analysis

The decision to employ the PLS-SEM analysis stems from its ability to deeply explore variance. In PLS-SEM, the measurement model assesses the instrument’s reliability, while the structural model tests the hypotheses [58]. Thus, Smart Partial Least Squares (PLS) is selected alongside PLS-SEM due to concerns regarding its predictive purpose [58]. As suggested by Cain et al. (2017), an analysis of multivariate skewness and kurtosis indicates slight deviations from normality, justifying the use of Smart PLS as a nonparametric tool for data analysis [62].

4.1. Demographic Profile

The study analyzed respondents’ demographic attributes using IBM SPSS version 22. The demographic analysis results are presented in

Table 1. Respondents’ profile.

Variable	Category	Frequency	Percentage
Age	Below 25 years	0	0
	25–30 years	5	3.0
	31–35 years	63	37.3
	Above 35 years	101	59.8
	Total	169	100%
Gender	Male	151	89.3
	Female	18	10.7
	Total	169	100%
Education	Bachelors	0	0
	Masters	65	38.5
	PhD	0	0
	Others	104	61.5
	Total	169	100%
Designation	Executives	94	55.6
	Senior Executive	64	37.9
	Assistant Manager	7	4.1
	Manager	4	2.4
	Total	169	100%
Job Experience	Less than three years	96	56.8
	3–5 years	65	38.5
	6–10 years	8	4.7
	Above ten years	0	0
	Total	169	100%

Source: SPSS output.

. The age category is divided into four groups. No respondents were below 25 years of age, while 5 respondents (3.0%) were between 25 and 30 years, 63 respondents (37.3%) were between 31 and 35 years, and 101 respondents (59.8%) were above 35 years. Among the 169 respondents, 151 (89.3%) were male and 18 (10.7%) were female. Regarding education, none of the respondents reported a bachelor’s or PhD qualification in the predefined categories. Instead, 65 respondents (38.5%) held a master’s degree, while 104 respondents (61.5%) reported other professional qualifications. In the context of Pakistan’s textile and apparel sector, the “other” category typically includes professional diplomas, vocational certifications, technical training, or industry-specific qualifications in areas such as textile technology, merchandising, and supply chain operations, which are common in the fashion manufacturing industry. These qualifications are often obtained through specialized institutes and are widely recognized within the industry, particularly for operational and managerial roles. In terms of designation, 94 respondents (55.6%) were executives, 64 (37.9%) were senior executives, 7 (4.1%) were assistant managers, and 4 (2.4%) were managers. These positions generally involve operational coordination, supply chain planning, and quality management responsibilities, making them appropriate informants for evaluating supply chain practices. Regarding job experience, 96 respondents (56.8%) reported less than three years of experience in their current roles, 65 respondents (38.5%) had between 3 and 5 years of experience, and 8 respondents (4.7%) had between 6 and 10 years of experience, while none reported more than ten years of experience. These respondents are typically early- to mid-career professionals actively involved in day-to-day supply chain operations, which positions them well to provide informed responses regarding operational practices and performance.

4.2. Common Method Bias

To address the potential for common method bias (CMB), a concern given that all variables were collected from a single respondent in a single survey, we implemented both procedural and statistical remedies consistent with Podsakoff et al. (2016) [63]. Procedurally, respondents were assured of anonymity and confidentiality and were informed that there were no right or wrong answers, reducing evaluation apprehension and response distortion [63]. Items were also carefully worded to minimize ambiguity and reduce the likelihood of consistency motifs. Statistically, we employed multiple diagnostics. First, a full collinearity assessment was conducted following Kock (2015) [64], and all variance inflation factor (VIF) values were well below the conservative threshold of 3.3, indicating that CMB was unlikely to threaten the model [58]. Second, a Harman single-factor test was performed, and the first unrotated factor accounted for less than 40% of the variance, suggesting that no single factor dominated the dataset. Third, we used a marker-variable technique by including a theoretically unrelated construct in the model; the marker showed no significant correlations with the substantive variables, further confirming that CMB was not a systematic issue. Taken together, the procedural steps and multiple diagnostic tests provide strong evidence that common method bias does not materially affect the study’s results or interpretations.

4.3. Measurement Model

Given the study’s focus on predictive purposes, the acquisition of latent variable scores is necessary for further analysis. Employing Smart Partial Least Squares [58], the study aimed to establish the measurement model, necessitating two types of validity: convergent validity, ensuring that items measure specific constructs, and discriminant validity, confirming the distinctiveness of items for each construct. To achieve convergent validity for reflective measurement, loadings and average variance extracted (AVE) of ≥0.5, and composite reliability (CR) ≥ 0.7 are required [57,58]. The analysis presented in

Table 2. Validity results.

Construct	Items	Loadings	CR	AVE
Information System (IS)	IS1: Exchange quality information with the supplier.	0.721	0.852	0.536
	IS2: Exchange technical information with the supplier.	0.698
	IS3: Exchange information with the supplier on production and operations.	0.787
	IS4: Provides suppliers with demand forecast information.	0.753
	IS5: Customers can easily monitor the status of their orders.	0.698
Quality Management (QM)	QM1: Organizes different departments, and employees work together to resolve quality problems.	0.686	0.863	0.559
	QM2: The Company has a perfect quality information collection and evaluation system.	0.785
	QM3: Quality control methods have been fully applied.	0.820
	QM4: Improvements are identified in the service delivery process.	0.772
	QM5: The Firm knows the customers’ present and future needs.	0.664
Supply Chain Agility (SCA)	SCA1: The company can flexibly reconfigure supply chain resources to respond to strategic opportunities/challenges.	0.692	0.844	0.521
	SCA2: The company can promptly detect strategic opportunities and challenges (e.g., new competitors entering the market, new economic trends, new technologies, and new markets).	0.739
	SCA3: The company can detect supply changes on time.	0.665
	SCA4: The company can detect demand changes promptly.	0.771
	SCA5: The company can flexibly reconfigure supply chain resources to respond to supply changes.	0.736
Firm Performance (FP)	FP1: The company exchanges recommendations for continuous improvement.	0.792	0.844	0.576
	FP2: The company delivers undamaged orders each time.	0.838
	FP3: The company delivers accurate orders at all times.	0.676
	FP4: The company consistently meets deadlines as promised.	0.721

Source: SmartPLS output.

indicates that loadings, AVEs, and CRs exceeded the thresholds set by Hair et al. (2012) [58], suggesting no issues with convergent validity in the study.

To establish discriminant validity, all Hetrotrait–Monotrait (HTMT) ratios need to be ≤0.9, as highlighted by Franke and Sarstedt (2019) [65]. The outcomes of the HTMT ratio assessment, presented in

Table 3. Heterotrait–Monotrait (HTMT) Ratios.

	FP	QM	SCA	IS
FP
QM	0.548
SCA	0.872	0.536
IS	0.821	0.345	0.715

, spanned from 0.345 to 0.872. As all the recorded values were below 0.9, this indicates that the study successfully demonstrated discriminant validity.

4.4. Structural Model

Before conducting hypothesis testing, it was crucial to verify that multicollinearity was not a significant issue in the study. Accordingly, Variance Inflation Factor (VIF) values needed to be ≤ 3.3. As all VIF values were below 3.3, this confirms that multicollinearity was not a concern. Subsequently, the study used bootstrapping with 5000 resamples to test hypotheses. This involved checking whether the beta value aligned with the direction of the hypothesis, whether the t-value was ≥ 1.645, whether the p-value was ≤ 0.05, and ensuring that no zero values were present within the lower level (LL) and upper level (UL) of the bias-corrected confidence interval [57].

The study formulated five direct hypotheses and two mediation hypotheses. H1 proposed a positive relationship between Information Sharing (IS) and Supply Chain Agility (SCA). The analysis demonstrated a significant positive effect of IS on SCA (β = 0.478, p ≤ 0.001), thereby supporting H1. Similarly, H2 suggested a positive association between IS and Firm Performance (FP). The analysis revealed a significant positive effect of IS on FP (β = 0.351; p ≤ 0.001), thus supporting H2. Moreover, the study indicated positive relationships between Quality Management (QM), SCA, and FP. Specifically, H3 posited a relationship between QM and SCA (β = 0.310, p ≤ 0.001), while H4 proposed a relationship between QM and FP (β = 0.147, p ≤ 0.05). Furthermore, the analysis confirmed a positive relationship between SCA and FP (β = 0.429, p ≤ 0.001), supporting H5.

Table 4. Path Coefficients.

Hypothesis	Path	Beta (β)	t Statistics	p-Values
H1	IS →SCA	0.478	6.947	0.000
H2	IS → FP	0.351	4.810	0.000
H3	QM → SCA	0.310	4.609	0.000
H4	QM → FP	0.147	2.820	0.005
H5	SCA → FP	0.429	6.817	0.000
H6	IS → SCA → FP	0.205	4.659	0.000
H7	QM → SCA → FP	0.133	3.665	0.000

demonstrates that only H6 exhibits a negligible effect size; the supported hypotheses exhibit high effect sizes.

The mediation analysis, following Preacher and Hayes (2008) [66], involved bootstrapping the indirect effect to assess mediation. Both Hypotheses H6 and H7 were supported. SCA was found to positively mediate the relationship between IS and FP. The analysis revealed a significant indirect effect (β = 0.133, p ≤ 0.001, t-value = 3.665), with no zero values within the bias-corrected confidence interval, indicating that SCA mediates the relationship between IS and FP. Similarly, for H7, the analysis demonstrated that SCA mediates the relationship between QM and FP. The results indicated a significant indirect effect (β = 0.205, p ≤ 0.001, t-value = 4.659), thereby supporting H7. Table 4 presents the results for the mediation hypotheses, and Figure 2 illustrates the structural model of the study. The mediation analysis indicates partial mediation. Both information sharing and quality management exhibit significant direct effects on firm performance, while their indirect effects through supply chain agility are also significant. This suggests that agility acts as a complementary mechanism through which these operational practices enhance firm performance rather than serving as the sole transmission pathway.

4.5. R-Square and Blindfolding

The structural model assessment revealed that Information Sharing (IS) and Quality Management (QM) together accounted for 41% of the variance in Supply Chain Agility (SCA), indicating substantial explanatory power. In turn, IS, QM, and SCA collectively accounted for 59% of the variance in Firm Performance (FP), demonstrating strong model relevance. To evaluate the practical importance of the predictors, effect sizes (f²) were calculated following Rosenthal et al.’s (1994) [67] guidelines, where 0.02, 0.15, and 0.35 represent small, medium, and large effects, respectively. Among the supported relationships, SCA exhibited the largest f² value on FP, indicating that agility is the most influential predictor of firm performance. IS and QM showed small-to-medium effect sizes on SCA, suggesting that both contribute meaningfully to agility development, with IS exerting a comparatively stronger influence. Predictive relevance was assessed using Q² (cross-validated redundancy) values for the two endogenous constructs. According to Tenenhaus et al. (2005), Q² values greater than zero confirm predictive accuracy [68]. The Q² values obtained for SCA (0.200) and FP (0.320) were both positive and well above zero, indicating that the model possesses adequate predictive capability. Collectively, these statistics confirm not only the model’s statistical significance but also its practical relevance and predictive strength.

5. Discussion

The results of this study provide a deeper understanding of how information sharing, quality management, and supply chain agility collectively influence operational performance in textile and apparel supply chains. While the empirical data were collected from firms operating in Pakistan, the relationships observed reflect broader dynamics characteristic of industries with high demand volatility, short product life cycles, and strong competitive pressures. In such environments, firms must continuously coordinate information flows, maintain operational reliability, and adapt quickly to market changes. The findings reinforce the view that these capabilities, while conceptually distinct, interact as an integrated system that enables firms to sense, interpret, and respond to rapidly shifting market requirements. This observation aligns with emerging evidence suggesting that supply chain capabilities rarely operate independently; rather, they function as interdependent capability bundles that jointly enhance organizational responsiveness and performance when effectively aligned [5,52,57].

The significant relationship between quality management and supply chain agility (H1) indicates that structured quality routines and process discipline contribute to the development of agile operational capabilities. Quality management systems promote standardized procedures, reduce process variability, and enhance coordination across production and supply activities. These mechanisms strengthen organizational readiness to respond to unexpected disruptions or changes in demand conditions. In industries where supplier reliability, material quality, and production consistency are critical operational concerns, quality management practices can create the stability necessary for rapid operational adjustments. This finding supports prior studies demonstrating that quality-oriented processes improve firms’ ability to implement responsive actions and strengthen agility-driven competitiveness [37,69]. More broadly, the results suggest that quality management functions not only as a mechanism for maintaining operational control but also as a foundational capability that enables firms to reconfigure processes and coordinate resources more effectively under conditions of uncertainty. Recent research similarly highlights that quality practices enhance agility by improving coordination and reducing internal frictions that delay decision-making and operational responses [30].

The results also show that information sharing has the strongest effect on supply chain agility (H2), underscoring the importance of timely, accurate information flows in dynamic supply chain environments. Effective information sharing improves visibility across supply chain partners, allowing firms to detect demand shifts, adjust production schedules, and coordinate procurement decisions more rapidly. When firms exchange operationally relevant data, such as inventory levels, demand forecasts, and order status, they enhance their ability to anticipate changes and respond proactively rather than reactively. This mechanism strengthens the sensing capability that underpins agile decision-making processes. From a theoretical perspective, this finding is consistent with dynamic capability theory, which emphasizes the role of sensing mechanisms in identifying emerging opportunities and threats in turbulent environments [27,34]. It also aligns with prior empirical research demonstrating that information visibility and communication efficiency are central drivers of supply chain agility and responsiveness [45,51]. The stronger influence of information sharing relative to quality management suggests that while quality practices support operational execution, information flows primarily enhance the organization’s ability to detect and interpret environmental signals.

The strong, significant relationship between supply chain agility and firm performance (H3) further reinforces the theoretical argument that agility is a critical dynamic capability in volatile supply chain environments. Agile supply chains allow firms to respond rapidly to demand fluctuations, modify production priorities, and coordinate supply network activities more efficiently. These capabilities are particularly valuable in industries characterized by rapidly changing consumer preferences and compressed product cycles. The findings therefore support existing scholarship showing that supply chain agility contributes to improved operational performance by enhancing delivery reliability, responsiveness, and coordination efficiency [1,35,51]. From a broader perspective, the results suggest that agility acts as the mechanism through which operational capabilities are translated into performance outcomes. Firms that develop the ability to rapidly sense changes, adjust operational processes, and coordinate supply chain partners are better positioned to maintain reliability and efficiency under conditions of market uncertainty. Consequently, the strong SCA → FP relationship observed in this study highlights the strategic importance of agility as a capability that transforms operational practices into improved performance outcomes across dynamic supply chain environments.

The direct effect of quality management on firm performance (H4), although statistically significant, was weaker than the effects observed for information sharing and supply chain agility. This result suggests that quality management contributes to performance primarily by strengthening the operational foundations that support effective supply chain execution. Quality practices such as standardized procedures, process monitoring, and continuous improvement help reduce operational errors and improve consistency in production and delivery processes. However, the relatively smaller direct effect indicates that the benefits of quality management are often realized indirectly through other operational capabilities, particularly agility. This interpretation is consistent with prior research showing that quality management strengthens organizational processes and coordination mechanisms, enabling firms to respond more effectively to environmental changes [37,52]. In other words, quality management enhances operational reliability, which in turn facilitates the development of agile responses to dynamic market conditions.

The results also demonstrate a strong direct relationship between information sharing and firm performance (H5), underscoring the importance of transparent, timely information flows in supply chain decision-making. Information sharing enhances visibility across supply chain partners and improves coordination among operational activities such as procurement, production planning, and distribution. When firms exchange accurate and timely information regarding demand patterns, inventory levels, and order status, they are better able to align supply chain activities with market requirements. This improved alignment reduces uncertainty and enhances operational efficiency. The findings therefore support earlier research suggesting that information transparency and communication efficiency play a central role in improving supply chain performance and organizational responsiveness [29]. More broadly, the results indicate that information sharing functions as a strategic capability that strengthens firms’ ability to sense and interpret changes in the external environment.

The mediation results for H6 and H7 constitute one of the study’s key contributions. The findings indicate that both information sharing and quality management influence firm performance not only directly but also indirectly through supply chain agility. This confirms that agility functions as an important mechanism through which operational practices translate into improved performance outcomes. Specifically, the QM → SCA → FP mediation suggests that quality management practices establish process stability and coordination structures that support rapid operational adjustments when market conditions change. Similarly, the IS → SCA → FP pathway indicates that information richness and visibility strengthen firms’ sensing capabilities, enabling them to identify emerging market signals and respond quickly through agile supply chain processes. These findings align with recent research demonstrating that organizational capabilities, such as digital integration, operational coordination, and quality management, often improve performance by enhancing agility, flexibility, and resilience mechanisms within supply chains [27,33,70,71].

Taken together, these results deepen theoretical understanding of how operational capabilities interact within supply chain systems. The findings suggest that supply chain agility should not be viewed merely as an operational outcome but rather as a dynamic capability that integrates sensing and responding mechanisms. Information sharing strengthens the organization’s ability to sense environmental changes, while quality management enhances the reliability and coordination required to respond effectively. When these capabilities operate together, they create a coherent capability system that enables firms to manage uncertainty, improve operational responsiveness, and enhance performance. Although the empirical analysis is based on firms operating in Pakistan’s textile and apparel sector, the underlying capability interactions identified in this study may also be relevant to supply chains operating in other industries characterized by demand volatility, rapid product cycles, and competitive pressure. Consequently, the findings contribute to the broader literature on supply chain agility, quality management, and information processing by clarifying how these capabilities jointly influence performance in dynamic supply chain environments.

5.1. Research Implications

5.1.1. Theoretical Implications

This study contributes to theory by clarifying how operational capabilities interact to produce supply chain agility and performance, rather than treating them as independent predictors. While prior studies have separately linked information sharing, quality management, and agility to firm performance, the theoretical novelty of this research lies in demonstrating the mechanism through which these capabilities interact within a unified capability system. First, the study advances Information Processing Theory (IPT) by showing that information sharing alone does not automatically translate into improved performance. Instead, its value emerges when firms possess the capability to convert information into rapid operational responses. The results indicate that supply chain agility acts as this conversion mechanism, transforming information visibility into effective decision-making and coordinated actions. This extends IPT by clarifying that in highly uncertain environments, performance improvements depend not only on information availability but also on the organizational capability to process and act upon that information quickly. Second, the study extends Quality Management Theory (QMT) by demonstrating that quality management practices contribute to more than operational control and consistency. The findings suggest that standardized processes, continuous improvement routines, and coordination mechanisms generated through quality management create the operational stability required for agile responses. In this way, quality management functions as a structural capability that enables organizations to reconfigure processes more effectively when market conditions change. This perspective helps reconcile inconsistent findings in previous research regarding the direct versus indirect performance effects of quality management. Third, the study enriches the Resource-Based View (RBV) by showing that information sharing and quality management operate as complementary organizational resources whose performance impact is realized through the dynamic capability of supply chain agility. The results indicate that these resources do not, in and of themselves, produce competitive advantage; rather, agility enables firms to integrate and operationalize them to respond effectively to environmental changes. Taken together, the study contributes theoretically by proposing and empirically validating a capability interaction framework in which information sharing strengthens sensing capacity, quality management provides operational stability, and supply chain agility integrates these mechanisms to generate performance outcomes. This perspective advances the understanding of how operational capabilities combine to form dynamic capability systems in volatile supply chain environments.

5.1.2. Managerial Implications

The findings of this study provide practical insights for managers operating in textile and apparel supply chains in Pakistan, particularly in environments characterized by volatile demand and short product life cycles. The results highlight the importance of structured information sharing, effective quality management practices, and supply chain agility in improving operational performance. First, firms should prioritize targeted and timely information-sharing practices across supply chain partners. Rather than exchanging broad or generic information, managers should focus on sharing operationally relevant data such as short-term demand forecasts, inventory levels, and order status updates. Timely exchange of such information can help suppliers adjust production schedules, procurement decisions, and replenishment quantities more effectively, thereby reducing stockouts, excess inventory, and operational delays. Digital communication tools, shared information platforms, and integrated enterprise systems can further enhance the speed and accuracy of information flows across the supply chain. Second, the findings suggest that quality management practices play a critical role in strengthening supply chain responsiveness. Firms should implement structured quality control mechanisms, including standardized production procedures, regular supplier evaluations, and systematic monitoring of product quality during manufacturing processes. Continuous training of operational staff and the implementation of feedback mechanisms on production floors can help identify defects early and reduce rework, thereby improving operational reliability and delivery performance. Third, managers should focus on developing supply chain agility by improving operational flexibility and coordination across functional areas. This may involve adopting flexible manufacturing processes, maintaining alternative sourcing options for critical inputs, and improving coordination between design, sourcing, production, and distribution functions. Shorter production cycles and more frequent replenishment decisions based on updated market information can also help firms respond more effectively to changing customer preferences. Importantly, the results indicate that information sharing and quality management should be implemented in an integrated manner to support agility development. Information transparency enhances the ability of firms to sense market changes, while structured quality practices improve operational execution. When these practices are aligned with agile decision-making processes, firms can enhance delivery reliability, order accuracy, and overall supply chain responsiveness. Such integrated capability development can help firms improve operational performance and maintain competitiveness in dynamic apparel supply chain environments.

5.2. Conclusions

This study investigated the impact of quality management and supply chain information sharing on supply chain agility and how, in turn, this enhances firm performance in the fashion industry. The findings demonstrate that agility significantly enhances a firm’s ability to sense and respond to market fluctuations by improving information quality, facilitating timely decision-making, and facilitating coordinated actions across supply chain partners. Consistent with prior research, our results confirm that agility is indispensable in fast-moving sectors where demand shifts rapidly and product life cycles are short. Moreover, the evidence shows that supply chain agility substantially boosts financial and operational outcomes, including sales growth, market share, productivity, time-to-market, and customer satisfaction, aligning with earlier findings by Chan et al. (2017) [24] and DeGroote and Marx (2013) [51].

The fashion industry’s supply chain is inherently unpredictable, requiring firms to anticipate demand fluctuations and continually adapt their operations. Our results support the argument that agility serves as a critical mechanism for managing uncertainty, satisfying evolving customer needs, and maintaining competitive advantage in time-sensitive markets. This finding reinforces earlier work by Suifan et al. (2020), who identified agility as a key mediator in enhancing supply chain outcomes under turbulent conditions [12].

Additionally, the study highlights the strategic value of integrated information sharing (IS) in elevating firm performance. By improving visibility, coordination, and responsiveness, SCIS with agility features provides a practical solution for addressing the operational challenges inherent in dynamic fashion markets. While previous research has emphasized the role of quality management in improving long-term performance, our study extends this literature by demonstrating how information sharing complements quality practices and jointly contributes to the improvement of agility-driven performance.

Overall, this study offers valuable implications for managers seeking to improve performance through enhanced operational practices. Managers should adopt a holistic perspective that integrates quality management, information sharing, and agility capabilities to remain competitive in volatile environments. Continuous improvement, proactive communication, and flexible decision-making structures are essential for sustaining superior performance in fashion supply chains. By offering empirical evidence from Pakistan’s fashion sector, this study contributes to the broader literature on agile supply chain management and provides a foundation for future research exploring similar models in other industries or regional contexts.

5.3. Limitations and Future Recommendations

The current study contains several limitations that should be acknowledged to contextualize the findings and guide future research. First, the data were collected via self-reported questionnaires, which may introduce common-method bias and perceptual subjectivity. Although procedural and statistical remedies were applied, self-reported measures cannot fully eliminate the risk of inflated relationships among constructs. Second, the study was conducted exclusively within Pakistan’s fashion industry, which limits the generalizability of the results. Cultural, structural, and institutional differences across countries can influence the operation of quality management practices, information sharing processes, and agility capabilities within supply chains. Therefore, the relationships established in this context may not be fully applicable in developed economies or in industries with varying technological maturity levels. Third, the sample size and reliance on single-source data pose additional constraints. Data drawn from respondents belonging to similar hierarchical levels or functional roles may not capture the full complexity of supply chain dynamics, potentially limiting the robustness of the findings.

These limitations open several avenues for future research. First, researchers may expand the sample size and diversify the demographic and industrial contexts to verify whether the effects of quality management, supply chain information sharing, and agility remain consistent across regions and sectors. Comparative multi-country studies would provide deeper insights into how institutional differences shape supply chain capabilities and performance outcomes. Second, future studies should incorporate objective performance indicators, such as inventory turnover, lead-time reduction, defect rates, and financial metrics, to complement perceptual data and strengthen construct validity. Third, employing multi-informant designs that collect responses from suppliers, buyers, managers, and operational staff could provide a more nuanced understanding of supply chain relationships and reduce single-respondent bias. Moreover, longitudinal research designs would allow scholars to examine how agility capabilities develop over time and to observe causal relationships more definitively. Finally, future research could explore moderating or boundary conditions, such as digital transformation readiness, environmental turbulence, firm size, or technology adoption, that may influence the effectiveness of information sharing and quality management in developing supply chain agility.