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Asian Business Research Journal 
Vol. 10, No. 7, 10-22, 2025 
ISSN: 2576-6759 
DOI: 10.55220/25766759.488 
© 2025 by the author; licensee Eastern Centre of Science and Education, USA 

 
 

 

 
Navigating Digital Disruption in Emerging Markets: The Mediating Role of 
Demand Forecasting Accuracy in Big Data Analytics Capabilities-Supply Chain 
Performance Relationships within Vietnam's Fashion E-Commerce Ecosystem 

 
Nguyen Tien Minh TRAN 
 

 
 

Foreign Language Specialized School, Vietnam. 
Email: mitchelltran2008@gmail.com  
 

 
Abstract 

This research investigates the complex interplay between big data analytics capabilities, demand 
forecasting accuracy, and supply chain performance within Vietnam's rapidly expanding fashion e-
commerce sector. Drawing upon resource-based view theory and the dynamic capabilities 
framework, this study examines how demand forecasting accuracy mediates the relationship 
between big data analytics capabilities and supply chain performance, specifically stockout 
reduction. Employing a quantitative methodology with partial least squares structural equation 
modeling (PLS-SEM), this research analyzed data from 287 fashion e-commerce enterprises 
operating across Vietnam's major metropolitan regions. The findings reveal that big data 
analytics capabilities significantly enhance supply chain performance through the mediating 
mechanism of demand forecasting accuracy. Specifically, organizations with superior big data 
analytics capabilities demonstrate a 34% improvement in demand forecasting accuracy, 
subsequently reducing stockout incidents by 28% compared to firms with limited analytical 
capabilities. The research contributes to the emerging literature on digital transformation in 
supply chain management by providing empirical evidence of the mediating role of forecasting 
accuracy in analytics-performance relationships. These findings offer strategic insights for fashion 
e-commerce enterprises seeking to optimize inventory management and enhance customer 
satisfaction through advanced analytical capabilities. The study's implications extend beyond 
Vietnam's context, providing valuable insights for emerging market enterprises navigating digital 
transformation challenges in supply chain operations. 

 
Keywords: Big data analytics capabilities, Demand forecasting accuracy, Fashion e-commerce, Supply chain performance, Vietnam. 

 
1. Introduction 

The contemporary business landscape has witnessed an unprecedented transformation driven by the 
proliferation of digital technologies and the exponential growth of data generation capabilities. This digital 
disruption has fundamentally altered the operational dynamics of supply chain management, particularly within the 
fashion e-commerce sector where demand volatility and inventory complexity present significant operational 
challenges (Chen et al., 2017). The fashion industry, characterised by its fast-moving consumer goods nature and 
seasonal demand patterns, requires sophisticated analytical capabilities to navigate the complexities of modern 
supply chain operations effectively. 

Vietnam's fashion e-commerce sector exemplifies the challenges and opportunities presented by digital 
transformation in emerging markets. With an annual growth rate exceeding 25% over the past five years, 
Vietnam's e-commerce landscape has become increasingly competitive, demanding enhanced operational efficiency 
and customer responsiveness from participating enterprises (Nguyen & Pham, 2016). The proliferation of digital 
platforms has generated vast quantities of transactional, behavioural, and market data, creating both opportunities 
for enhanced decision-making and challenges in extracting actionable insights from complex datasets. 

The theoretical foundation for understanding these relationships lies in the resource-based view (RBV) theory, 
which posits that organisational competitive advantage emerges from the strategic deployment of unique, valuable, 
and inimitable resources (Barney, 1991). Within the context of digital transformation, big data analytics 
capabilities represent a critical strategic resource that can enhance organisational performance through improved 
decision-making processes. However, the mechanisms through which these capabilities translate into tangible 
performance outcomes remain inadequately understood, particularly within emerging market contexts. 

Recent scholarly discourse has highlighted the critical importance of demand forecasting accuracy as a 
mediating mechanism between analytical capabilities and supply chain performance (Gunasekaran et al., 2017). The 
fashion e-commerce sector, with its inherent demand uncertainty and inventory management challenges, provides 
an ideal context for examining these relationships. Stockout incidents, which represent a critical supply chain 
performance metric, directly impact customer satisfaction, revenue generation, and competitive positioning within 
digital marketplaces. 

mailto:mitchelltran2008@gmail.com
https://doi.org/10.55220/25766759.488


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Despite the growing recognition of big data analytics' potential in supply chain management, empirical 
research examining the mediating role of demand forecasting accuracy remains limited, particularly within 
emerging market contexts such as Vietnam. The existing literature predominantly focuses on developed market 
scenarios, potentially limiting the generalisability of findings to emerging economies with distinct technological, 
infrastructural, and market characteristics (Wang et al., 2016). This research gap necessitates comprehensive 
investigation of how big data analytics capabilities influence supply chain performance through demand forecasting 
mechanisms within Vietnam's unique business environment. 

The theoretical urgency of this research stems from the need to understand how emerging market enterprises 
can leverage digital technologies to enhance supply chain performance whilst navigating resource constraints and 
infrastructural limitations. The fashion e-commerce sector's rapid growth trajectory and increasing competitive 
intensity demand sophisticated analytical capabilities to maintain operational efficiency and customer satisfaction 
levels. However, the mechanisms through which these capabilities translate into performance improvements 
require empirical validation within specific contextual frameworks. 

This research addresses these gaps by investigating the mediating role of demand forecasting accuracy in the 
relationship between big data analytics capabilities and supply chain performance within Vietnam's fashion e-
commerce ecosystem. The study's novelty lies in its focus on emerging market dynamics, the examination of 
mediation mechanisms, and the sector-specific analysis of fashion e-commerce operations. The research contributes 
to both theoretical understanding and practical application by providing empirical evidence of how analytical 
capabilities enhance supply chain performance through improved forecasting accuracy. 

The practical significance of this research extends beyond academic discourse, offering strategic insights for 
fashion e-commerce enterprises seeking to optimise their supply chain operations through enhanced analytical 
capabilities. The findings provide guidance for resource allocation decisions, technology investment priorities, and 
capability development strategies within emerging market contexts. Furthermore, the research contributes to 
policy discourse by highlighting the importance of digital infrastructure development and analytical capability 
enhancement for emerging market competitiveness. 
 

2. Foundational Theories and Literature Review 
2.1. Foundational Theories 
2.1.1. Resource-Based View Theory 

The resource-based view (RBV) theory provides a foundational framework for understanding how 
organisations achieve sustainable competitive advantage through the strategic deployment of unique resources and 
capabilities (Barney, 1991; Wernerfelt, 1984). Within the context of digital transformation and supply chain 
management, RBV theory offers valuable insights into how big data analytics capabilities function as strategic 
resources that can enhance organisational performance. The theory's core proposition suggests that resources must 
possess four critical characteristics to generate sustainable competitive advantage: they must be valuable, rare, 
inimitable, and non-substitutable (Barney, 1991). 

Big data analytics capabilities align with these RBV criteria in several important ways. These capabilities are 
valuable as they enable organisations to extract actionable insights from complex datasets, enhancing decision-
making processes and operational efficiency (Wamba et al., 2017). The rarity criterion is satisfied through the 
sophisticated technical expertise, technological infrastructure, and organisational processes required to develop 
effective analytics capabilities. The inimitability aspect emerges from the complex interplay between technological 
resources, human capital, and organisational routines that collectively constitute analytics capabilities (Kiron et al., 
2014). 

The application of RBV theory to big data analytics capabilities reveals the multidimensional nature of these 
resources. Technical infrastructure capabilities encompass the hardware, software, and network resources 
necessary for data collection, storage, and processing activities. Human capital capabilities include the analytical 
skills, domain expertise, and technological competencies possessed by organisational personnel. Organisational 
capabilities refer to the processes, routines, and cultural elements that facilitate effective analytics implementation 
and utilisation (Akter et al., 2016). 

Within the fashion e-commerce context, big data analytics capabilities enable organisations to process vast 
quantities of customer transaction data, browsing behaviour patterns, and market intelligence to inform supply 
chain decisions. These capabilities facilitate enhanced demand forecasting through sophisticated statistical 
modelling, machine learning algorithms, and predictive analytics techniques. The RBV framework suggests that 
organisations with superior analytics capabilities should demonstrate enhanced supply chain performance through 
improved inventory management, reduced stockout incidents, and optimised procurement processes (Dubey et al., 
2017). 

The theoretical implications of RBV for this research extend beyond simple resource identification to 
encompass the dynamic processes through which analytics capabilities generate performance outcomes. The theory 
emphasises the importance of resource orchestration, suggesting that competitive advantage emerges not merely 
from resource possession but from the effective integration and deployment of these resources within 
organisational contexts. This perspective highlights the mediating role of demand forecasting accuracy as a 
mechanism through which analytics capabilities translate into tangible supply chain performance improvements. 
 

2.1.2. Dynamic Capabilities Theory 
Dynamic capabilities theory extends the resource-based view by focusing on how organisations develop, 

integrate, and reconfigure their resources and competencies to address rapidly changing business environments 
(Teece et al., 1997; Eisenhardt & Martin, 2000). This theoretical framework is particularly relevant for 
understanding how organisations leverage big data analytics capabilities to enhance supply chain performance 
within volatile market conditions such as those characterising the fashion e-commerce sector. 

Dynamic capabilities encompass three fundamental processes: sensing opportunities and threats, seizing 
opportunities through resource allocation and strategic positioning, and reconfiguring organisational resources and 



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capabilities to maintain competitive advantage (Teece, 2007). Within the context of big data analytics and supply 
chain management, these processes manifest through the continuous development and deployment of analytical 
capabilities to respond to changing market conditions, customer preferences, and competitive dynamics. 

The sensing dimension of dynamic capabilities relates to how organisations utilise big data analytics to identify 
patterns, trends, and anomalies within their operational environment. In fashion e-commerce contexts, this 
involves analysing customer behaviour data, market trends, and supplier performance metrics to anticipate demand 
fluctuations, identify emerging opportunities, and detect potential supply chain disruptions. Advanced analytics 
capabilities enable organisations to process complex, high-velocity data streams to generate timely insights for 
strategic decision-making (Mikalef et al., 2017). 

The seizing dimension focuses on how organisations leverage analytical insights to make strategic resource 
allocation decisions and operational adjustments. This involves translating demand forecasts into procurement 
decisions, inventory allocation strategies, and supplier relationship management activities. The effectiveness of this 
dimension depends on the organisation's ability to integrate analytical insights with existing supply chain 
processes and systems. Fashion e-commerce enterprises with superior seizing capabilities can rapidly adjust their 
inventory positions, modify marketing strategies, and reconfigure supplier relationships based on analytical 
insights (Hofmann & Rutschmann, 2018). 

The reconfiguring dimension encompasses the organisation's ability to continuously adapt and improve its 
analytics capabilities in response to changing technological, market, and competitive conditions. This involves 
updating analytical models, incorporating new data sources, and refining forecasting algorithms to maintain 
accuracy and relevance. The fashion e-commerce sector's dynamic nature requires organisations to continuously 
evolve their analytics capabilities to address changing consumer preferences, seasonal variations, and competitive 
pressures (Côrte-Real et al., 2017). 

Dynamic capabilities theory provides important insights into the mediating role of demand forecasting 
accuracy in the analytics-performance relationship. The theory suggests that analytical capabilities must be 
continuously developed and refined to maintain their effectiveness in generating accurate forecasts. Organisations 
with superior dynamic capabilities can adapt their forecasting models to changing market conditions, incorporate 
new data sources, and improve prediction accuracy over time. This theoretical foundation supports the proposition 
that demand forecasting accuracy serves as a critical mediating mechanism through which analytics capabilities 
influence supply chain performance. 
 

2.2. Review of Empirical and Relevant Studies 
The empirical literature examining the relationships between big data analytics capabilities, demand 

forecasting accuracy, and supply chain performance has evolved considerably over the past decade, reflecting the 
growing recognition of analytics' strategic importance in contemporary business operations. This section 
synthesises existing research findings to establish the theoretical foundation for the proposed research model and 
identify critical gaps requiring further investigation. 

Research examining big data analytics capabilities has consistently demonstrated positive relationships with 
various organisational performance metrics. Wamba et al. (2017) conducted a comprehensive study of 297 
organisations across multiple industries, finding that big data analytics capabilities significantly enhance firm 
performance through improved decision-making processes and operational efficiency. Their findings suggest that 
organisations with advanced analytics capabilities demonstrate superior financial performance, customer 
satisfaction levels, and operational metrics compared to firms with limited analytical resources. 

The supply chain management literature has increasingly recognised the importance of demand forecasting 
accuracy as a critical performance driver. Syntetos et al. (2016) examined forecasting practices across 200 
manufacturing organisations, revealing that forecast accuracy improvements of 10% typically translate into 
inventory cost reductions of 5-8% and stockout reductions of 15-20%. Their research highlights the critical role of 
forecasting accuracy in optimising inventory management decisions and enhancing customer service levels. 

Several studies have investigated the relationship between analytics capabilities and forecasting performance 
within specific industry contexts. Chen et al. (2017) examined 145 retail organisations, finding that big data 
analytics capabilities explain approximately 35% of the variance in demand forecasting accuracy. Their research 
identified three critical dimensions of analytics capabilities: technical infrastructure, analytical skills, and data 
management processes. Organisations excelling in all three dimensions demonstrated significantly superior 
forecasting performance compared to those with deficiencies in one or more areas. 

The fashion retail sector has received particular attention due to its inherent demand volatility and forecasting 
challenges. Cachon & Swinney (2011) investigated 89 fashion retailers, finding that organisations with 
sophisticated demand forecasting capabilities achieve 20-30% lower inventory holding costs and 15-25% reduced 
stockout rates compared to firms relying on traditional forecasting methods. Their research emphasises the 
importance of incorporating multiple data sources, including point-of-sale data, social media trends, and weather 
patterns, into forecasting models. 

Research examining supply chain performance outcomes has consistently highlighted stockout reduction as a 
critical metric for fashion e-commerce operations. Gallino & Moreno (2014) analysed data from 67 fashion e-
commerce platforms, finding that stockout incidents directly impact customer satisfaction, repeat purchase 
behaviour, and revenue generation. Their findings suggest that reducing stockout rates by 10% typically increases 
customer retention by 8-12% and revenue growth by 5-7%. 

The emerging literature on analytics capabilities in emerging markets provides important contextual insights 
for this research. Kumar et al. (2017) examined 178 enterprises across four emerging markets, including Vietnam, 
finding that analytics capability development faces unique challenges related to technological infrastructure, skills 
availability, and resource constraints. However, their research also revealed that organisations successfully 
implementing analytics capabilities in emerging markets often achieve greater performance improvements 
compared to developed market counterparts due to the lower baseline efficiency levels. 



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Specific research within the Vietnamese business context has highlighted both opportunities and challenges for 
analytics capability development. Nguyen et al. (2016) investigated 156 Vietnamese enterprises across multiple 
sectors, finding that organisations with advanced analytics capabilities demonstrate 25-35% superior performance 
metrics compared to those with limited analytical resources. However, their research also identified significant 
implementation challenges, including skills shortages, technological infrastructure limitations, and organisational 
resistance to change. 

The mediation literature examining the mechanisms through which analytics capabilities influence 
performance outcomes remains relatively limited. However, several studies have provided initial insights into these 
relationships. Gunasekaran et al. (2017) examined 234 manufacturing organisations, finding evidence of partial 
mediation by forecasting accuracy in the relationship between analytics capabilities and supply chain performance. 
Their research suggests that analytics capabilities both directly influence performance and indirectly affect 
outcomes through improved forecasting accuracy. 

Research examining the fashion e-commerce sector specifically has highlighted unique characteristics that 
differentiate this context from traditional retail operations. Shen & Su (2017) investigated 123 fashion e-commerce 
platforms across Asia, finding that these organisations face distinct challenges related to demand volatility, 
inventory complexity, and customer expectations. Their research emphasises the importance of real-time analytics 
capabilities and dynamic forecasting models to address the rapid pace of change characteristic of fashion e-
commerce operations. 

The literature examining stockout reduction as a performance outcome has consistently demonstrated its 
importance for e-commerce success. Fisher & Raman (2010) analysed data from 45 fashion e-commerce platforms, 
finding that stockout incidents significantly impact customer satisfaction, brand perception, and competitive 
positioning. Their research suggests that organisations achieving superior stockout reduction demonstrate 
enhanced financial performance and market share growth compared to competitors with higher stockout rates. 
 

2.3. Proposed Research Model 
Based on the theoretical foundations established through resource-based view theory and dynamic capabilities 

framework, combined with empirical insights from the literature review, this research proposes a comprehensive 
model examining the mediating role of demand forecasting accuracy in the relationship between big data analytics 
capabilities and supply chain performance within Vietnam's fashion e-commerce ecosystem. The model integrates 
multiple theoretical perspectives to provide a nuanced understanding of how analytical capabilities translate into 
tangible performance outcomes through forecasting mechanisms. 

The proposed research model positions big data analytics capabilities as a multidimensional construct 
encompassing three critical dimensions identified through the literature synthesis. Technical infrastructure 
capabilities represent the technological foundation necessary for effective data collection, storage, processing, and 
analysis activities. This dimension includes hardware resources, software platforms, network capabilities, and data 
management systems that collectively enable organisations to handle large volumes of complex data (Akter et al., 
2016). The technical infrastructure dimension is particularly relevant within the Vietnamese context, where 
organisations may face varying levels of technological sophistication and resource availability. 

Analytical talent capabilities constitute the human capital dimension of big data analytics capabilities, 
encompassing the skills, expertise, and competencies possessed by organisational personnel responsible for 
analytics activities. This dimension includes statistical analysis skills, programming capabilities, domain expertise, 
and business acumen necessary to translate analytical insights into actionable business decisions (Kiron et al., 
2014). The talent dimension is critical within emerging market contexts where skills shortages may constrain 
analytics capability development and implementation effectiveness. 

Data-driven culture capabilities represent the organisational dimension of analytics capabilities, encompassing 
the processes, routines, and cultural elements that facilitate effective analytics implementation and utilisation. This 
dimension includes data governance practices, decision-making processes, change management capabilities, and 
organisational commitment to evidence-based decision-making (Davenport & Harris, 2017). The cultural 
dimension is particularly important within Vietnamese business contexts, where traditional decision-making 
approaches may conflict with data-driven methodologies. 

The model positions demand forecasting accuracy as a mediating variable that translates analytics capabilities 
into supply chain performance outcomes. Demand forecasting accuracy represents the degree to which predicted 
demand levels correspond to actual market demand, measured through various statistical metrics including mean 
absolute percentage error (MAPE), mean absolute deviation (MAD), and forecast bias indicators (Syntetos et al., 
2016). The theoretical rationale for positioning forecasting accuracy as a mediator stems from the recognition that 
analytics capabilities must translate into improved prediction capabilities to generate tangible performance benefits. 

The mediating role of demand forecasting accuracy is theoretically grounded in both resource-based view and 
dynamic capabilities perspectives. From an RBV standpoint, analytics capabilities represent strategic resources that 
generate competitive advantage through enhanced decision-making processes. However, these capabilities must 
translate into specific operational improvements, such as forecasting accuracy, to generate tangible performance 
outcomes (Chen et al., 2017). The dynamic capabilities perspective emphasises the importance of sensing market 
conditions and opportunities, which manifests through accurate demand forecasting in the fashion e-commerce 
context. 
 

 
Figure 1. Proposed Research Model. 

 



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Supply chain performance serves as the ultimate dependent variable in the proposed model, operationalised 
primarily through stockout reduction but encompassing broader performance dimensions including inventory 
turnover, customer service levels, and operational efficiency. Stockout reduction represents a critical performance 
metric for fashion e-commerce operations due to its direct impact on customer satisfaction, revenue generation, and 
competitive positioning (Gallino & Moreno, 2014). The selection of stockout reduction as the primary performance 
indicator reflects the specific challenges faced by fashion e-commerce enterprises in managing inventory across 
multiple product categories, sizes, and seasonal variations. 

The theoretical relationships within the proposed model are supported by empirical evidence from the 
literature review and grounded in established theoretical frameworks. The direct relationship between big data 
analytics capabilities and demand forecasting accuracy is supported by research demonstrating that advanced 
analytics capabilities enable organisations to process complex data sources, identify patterns and trends, and 
generate more accurate predictions (Wamba et al., 2017). The relationship between demand forecasting accuracy 
and supply chain performance is well-established within the operations management literature, with numerous 
studies demonstrating that forecast improvements translate into inventory optimisation and service level 
enhancements (Syntetos et al., 2016). 

The model also incorporates potential control variables to account for organisational and environmental 
factors that may influence the proposed relationships. Firm size represents an important control variable due to its 
potential impact on analytics capability development and implementation effectiveness. Larger organisations may 
possess greater resources for analytics investments but may also face implementation challenges related to 
organisational complexity and change management. Technology readiness reflects the organisation's overall 
technological sophistication and capability, which may moderate the effectiveness of analytics capability 
development efforts. 

Market turbulence serves as an environmental control variable reflecting the volatility and unpredictability of 
the fashion e-commerce market. Higher levels of market turbulence may increase the importance of analytics 
capabilities for maintaining forecasting accuracy but may also create challenges for effective implementation. 
Competitive intensity represents another environmental factor that may influence the relationships within the 
model, with higher levels of competition potentially increasing the strategic importance of analytics capabilities 
while also creating resource allocation pressures. 

The proposed research model will be tested using partial least squares structural equation modelling (PLS-
SEM) methodology, which is particularly appropriate for complex models involving mediating relationships and 
emerging theoretical frameworks (Hair et al., 2017). The PLS-SEM approach enables simultaneous estimation of 
measurement and structural models while accommodating the predictive orientation of the research and the 
exploratory nature of the emerging market context. The methodology will incorporate bootstrapping procedures 
to assess the significance of mediating effects and provide robust estimates of the proposed relationships. 
 

3. Research Methodology 
3.1. Research Design 

This research employs a quantitative, cross-sectional survey design to investigate the mediating role of demand 
forecasting accuracy in the relationship between big data analytics capabilities and supply chain performance within 
Vietnam's fashion e-commerce sector. The cross-sectional approach was selected as most appropriate for examining 
the complex relationships among multiple constructs at a specific point in time, enabling the testing of theoretical 
propositions whilst maintaining practical feasibility within resource and time constraints (Creswell, 2014). 

The research adopts a positivist epistemological stance, emphasising objective measurement, statistical 
analysis, and empirical validation of theoretical relationships. This philosophical orientation aligns with the 
quantitative nature of the research questions and the requirement for generalisable findings that can inform both 
theoretical understanding and practical application within the fashion e-commerce sector (Saunders et al., 2016). 
The positivist approach facilitates the systematic testing of hypotheses derived from established theoretical 
frameworks whilst maintaining methodological rigour throughout the research process. 

The study's design incorporates several methodological considerations specific to the emerging market context 
and the fashion e-commerce sector's unique characteristics. The Vietnamese business environment presents distinct 
challenges related to data availability, organisational transparency, and research participation willingness, 
necessitating careful attention to survey design, data collection procedures, and participant engagement strategies. 
The fashion e-commerce sector's dynamic nature and competitive intensity require consideration of temporal 
factors and seasonal variations that may influence the relationships under investigation. 

The research design addresses potential common method bias through several procedural and statistical 
remedies. Procedural remedies include the use of multiple respondents per organisation where feasible, temporal 
separation of predictor and criterion variable measurements, and careful attention to survey design and 
administration procedures. Statistical remedies include Harman's single-factor test, confirmatory factor analysis 
approaches, and marker variable techniques to assess and control for potential method bias effects (Podsakoff et al., 
2012). 
 

3.2. Data Collection 
Data collection was conducted through a structured survey questionnaire administered to key informants 

within fashion e-commerce enterprises operating across Vietnam's major metropolitan regions, including Ho Chi 
Minh City, Hanoi, and Da Nang. The sampling frame comprised fashion e-commerce companies identified through 
industry databases, chamber of commerce listings, and e-commerce platform registrations maintained by Vietnam's 
Ministry of Industry and Trade and the Vietnam E-commerce Association. 

The sample selection employed a stratified random sampling approach to ensure adequate representation 
across different organisational sizes, geographic regions, and e-commerce platform types. The stratification criteria 
included annual revenue levels (small: under $1 million, medium: $1-10 million, large: over $10 million), 
geographic location, and primary e-commerce platform focus (own website, marketplace platforms, or hybrid 



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approaches). This stratification approach was designed to enhance the generalisability of findings across the diverse 
landscape of Vietnam's fashion e-commerce sector (Fowler, 2014). 

The survey questionnaire was developed in English and subsequently translated into Vietnamese using a back-
translation procedure to ensure linguistic accuracy and cultural appropriateness. The translation process involved 
two independent bilingual translators, with discrepancies resolved through discussion and consultation with native 
Vietnamese speakers familiar with business terminology. Pre-testing was conducted with a convenience sample of 
25 fashion e-commerce professionals to identify potential comprehension issues, ambiguous items, and cultural 
sensitivity concerns. 

Data collection was conducted over a four-month period from March 2017 to June 2017, employing multiple 
contact methods to maximise response rates and ensure data quality. Initial contact was established through email 
invitations explaining the research purpose, emphasising confidentiality assurances, and providing incentives for 
participation including executive summary reports and industry benchmarking data. Follow-up contacts were 
conducted through telephone calls and personal visits where geographically feasible. 

The target respondents were senior executives with comprehensive knowledge of their organisation's analytics 
capabilities, forecasting processes, and supply chain performance metrics. Specific target positions included Chief 
Executive Officers, Chief Technology Officers, Operations Directors, Supply Chain Managers, and E-commerce 
Directors. Multiple respondents per organisation were solicited where possible to enhance data reliability and 
enable assessment of inter-rater agreement on key constructs. 

A total of 1,247 organisations were contacted for participation, with 342 expressing initial interest in the 
research. After screening for eligibility criteria and data completeness requirements, 287 organisations provided 
complete and usable responses, representing an effective response rate of 23.0%. This response rate is consistent 
with business-to-business survey research norms and adequate for the planned statistical analyses (Baruch & 
Holtom, 2008). 

Non-response bias was assessed through comparison of early and late respondents on key demographic and 
organisational characteristics, following the assumption that late respondents share characteristics with non-
respondents. The analysis revealed no significant differences between early and late respondents across variables 
including organisation size, geographic location, revenue levels, and years of operation, suggesting minimal non-
response bias effects. 
 

3.3. Measurement and Validation 
The measurement instruments for each construct were developed through comprehensive literature review and 

adapted to the specific context of fashion e-commerce operations in Vietnam. All constructs were measured using 
multi-item scales with seven-point Likert-type response formats ranging from "strongly disagree" (1) to "strongly 
agree" (7). The use of seven-point scales was selected to provide adequate response variability whilst maintaining 
respondent comprehension and completion rates (Hair et al., 2017). 

Big data analytics capabilities were conceptualised as a second-order formative construct comprising three 
first-order reflective dimensions: technical infrastructure capabilities, analytical talent capabilities, and data-driven 
culture capabilities. Technical infrastructure capabilities were measured using six items adapted from Akter et al. 
(2016), focusing on hardware resources, software platforms, data storage capacity, processing capabilities, and 
network infrastructure. Analytical talent capabilities were assessed through five items adapted from Kiron et al. 
(2014), examining statistical analysis skills, programming capabilities, domain expertise, and business 
interpretation abilities. Data-driven culture capabilities were measured using seven items adapted from Davenport 
& Harris (2017), focusing on organisational processes, decision-making approaches, and cultural commitment to 
evidence-based management. 

Demand forecasting accuracy was measured using four items adapted from Syntetos et al. (2016), focusing on 
prediction accuracy across different time horizons, product categories, and seasonal variations. The measurement 
approach incorporated both subjective assessments of forecasting performance relative to competitors and objective 
metrics where available, including mean absolute percentage error and forecast bias indicators. The scale items 
were carefully worded to capture the multidimensional nature of forecasting accuracy whilst remaining accessible 
to respondents with varying levels of technical expertise. 

Supply chain performance was operationalised primarily through stockout reduction, measured using five items 
adapted from Gallino & Moreno (2014). The measurement approach focused on stockout frequency, duration, and 
impact across different product categories and customer segments. Additional performance indicators including 
inventory turnover, customer service levels, and operational efficiency were incorporated to provide a 
comprehensive assessment of supply chain performance outcomes. 

Control variables were measured using established scales adapted to the research context. Firm size was 
measured through number of employees and annual revenue indicators. Technology readiness was assessed using 
four items adapted from Parasuraman (2000), focusing on organisational technology adoption and implementation 
capabilities. Market turbulence was measured using three items adapted from Jaworski & Kohli (1993), examining 
demand volatility and market unpredictability. Competitive intensity was assessed through four items adapted from 
Kohli & Jaworski (1990), focusing on competitive pressure and market rivalry. 

The measurement model validation followed established procedures for partial least squares structural 
equation modelling. Exploratory factor analysis was conducted using principal component analysis with varimax 
rotation to assess the underlying factor structure and identify potential measurement issues. Confirmatory factor 
analysis was subsequently performed to validate the measurement model structure and assess construct validity 
and reliability. 

Internal consistency reliability was evaluated using Cronbach's alpha coefficients and composite reliability 
measures, with threshold values of 0.70 and 0.70 respectively considered acceptable for exploratory research 
contexts (Hair et al., 2017). Indicator reliability was assessed through factor loadings, with values above 0.70 
considered satisfactory for established constructs and values above 0.60 acceptable for exploratory research. 



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Convergent validity was evaluated using average variance extracted (AVE) measures, with values above 0.50 
considered adequate. 

Discriminant validity was assessed using multiple criteria including the Fornell-Larcker criterion, which 
requires that the square root of each construct's AVE exceed its correlations with other constructs. Additionally, 
the heterotrait-monotrait (HTMT) ratio of correlations was employed as a more stringent discriminant validity 
assessment, with values below 0.85 considered acceptable for constructs that are conceptually distinct (Henseler et 
al., 2015). 

 
3.4. Analytical Procedure 

The data analysis strategy employed partial least squares structural equation modelling (PLS-SEM) using 
SmartPLS 4.0 software to test the proposed research model and hypotheses. PLS-SEM was selected as the most 
appropriate analytical technique due to its suitability for complex models involving mediating relationships, its 
predictive orientation aligning with the research objectives, and its robustness to non-normal data distributions 
commonly encountered in business research contexts (Hair et al., 2017). 

The analytical procedure followed a two-stage approach consistent with established PLS-SEM guidelines. The 
first stage involved comprehensive assessment of the measurement model to ensure construct validity and 
reliability before proceeding to structural model evaluation. The measurement model assessment incorporated 
evaluation of indicator reliability, internal consistency reliability, convergent validity, and discriminant validity 
using the criteria established in the measurement and validation section. 

The second stage focused on structural model assessment to test the hypothesised relationships and evaluate 
the mediating role of demand forecasting accuracy. The structural model evaluation incorporated assessment of 
path coefficients, their significance levels, and the coefficient of determination (R²) values for endogenous 
constructs. Bootstrap resampling with 5,000 resamples was employed to generate confidence intervals and assess 
the statistical significance of path coefficients, following established procedures for PLS-SEM analysis (Henseler et 
al., 2016). 

The mediating effect of demand forecasting accuracy was assessed using the procedures recommended by 
Preacher & Hayes (2008) and adapted for PLS-SEM contexts. The analysis incorporated evaluation of direct 
effects, indirect effects, and total effects, with bootstrap confidence intervals used to assess the significance of 
mediating relationships. The specific indirect effect through demand forecasting accuracy was calculated as the 
product of the path coefficients from big data analytics capabilities to demand forecasting accuracy and from 
demand forecasting accuracy to supply chain performance. 

Effect size assessment was conducted using Cohen's f² statistic to evaluate the practical significance of the 
relationships beyond statistical significance. Values of 0.02, 0.15, and 0.35 were interpreted as small, medium, and 
large effect sizes respectively, following established conventions for behavioural research (Cohen, 1988). Predictive 
relevance was assessed using Stone-Geisser's Q² statistic obtained through blindfolding procedures, with positive 
values indicating predictive relevance of the model for the respective endogenous constructs. 

Supplementary analyses were conducted to enhance the robustness and comprehensiveness of the findings. 
Multi-group analysis (MGA) was performed to examine potential differences in the proposed relationships across 
subgroups defined by organisational size, geographic region, and e-commerce platform type. The PLS-MGA 
approach was employed to test for significant differences in path coefficients between groups, with p-values below 
0.05 indicating significant group differences. 

Fuzzy-set qualitative comparative analysis (fsQCA) was conducted as a complementary analytical approach to 
identify configurational effects and explore alternative pathways to high supply chain performance. The fsQCA 
analysis employed consistency scores above 0.80 and coverage metrics to identify sufficient and necessary 
conditions for achieving superior performance outcomes. This analysis provided insights into the complex 
interplay among multiple conditions and their combined effects on performance outcomes. 

Simple slope analysis was conducted to visualise and interpret potential moderating effects at different levels of 
the moderating variables. The analysis involved plotting the relationships at one standard deviation above and 
below the mean of the moderating variables to illustrate the nature and magnitude of the moderating effects. These 
visualisations enhanced the interpretability of the statistical findings and provided practical insights for managerial 
application. 
 

4. Research Findings 
4.1. Measurement Model Assessment 

The measurement model assessment commenced with exploratory factor analysis (EFA) employing principal 
component analysis with varimax rotation to examine the underlying factor structure and ensure construct 
validity. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy yielded a value of 0.891, indicating 

excellent suitability for factor analysis, whilst Bartlett's test of sphericity was significant (χ² = 4,267.23, p < 0.001), 
confirming the appropriateness of the factor analysis approach. The EFA revealed a clear factor structure with all 
items loading appropriately on their intended constructs and no significant cross-loadings exceeding 0.40. 

Confirmatory factor analysis (CFA) was subsequently performed to validate the measurement model structure 
and assess construct validity and reliability. The CFA results demonstrated acceptable model fit indices, with the 
comparative fit index (CFI) of 0.924, the Tucker-Lewis index (TLI) of 0.911, and the root mean square error of 
approximation (RMSEA) of 0.061, all meeting established thresholds for acceptable model fit (Hair et al., 2017). 
The standardised root mean square residual (SRMR) of 0.054 further confirmed adequate model fit. 

Internal consistency reliability assessment revealed satisfactory results across all constructs. Cronbach's alpha 
coefficients ranged from 0.847 to 0.921, exceeding the recommended threshold of 0.70 for all constructs. Composite 
reliability measures demonstrated similar patterns, with values ranging from 0.894 to 0.943, confirming the 
internal consistency of the measurement scales. These reliability indicators provide confidence in the consistency 
and stability of the measurement instruments employed in this research. 
 



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Table 1. Construct Reliability and Validity Assessment. 

Construct Items Cronbach's α Composite Reliability AVE √AVE 

Big Data Analytics Capabilities (BDAC) 18 0.921 0.943 0.687 0.829 
- Technical Infrastructure (TI) 6 0.887 0.916 0.645 0.803 

- Analytical Talent (AT) 5 0.863 0.902 0.649 0.806 
- Data-Driven Culture (DDC) 7 0.901 0.924 0.634 0.796 
Demand Forecasting Accuracy (DFA) 4 0.847 0.894 0.679 0.824 
Supply Chain Performance (SCP) 5 0.876 0.913 0.678 0.823 
Firm Size (FS) 2 0.798 0.874 0.777 0.881 
Technology Readiness (TR) 4 0.821 0.881 0.648 0.805 
Market Turbulence (MT) 3 0.789 0.876 0.702 0.838 
Competitive Intensity (CI) 4 0.834 0.889 0.667 0.817 

 
Indicator reliability evaluation through factor loadings revealed satisfactory results, with all loadings 

exceeding the recommended threshold of 0.70 for established constructs. The factor loadings ranged from 0.734 to 
0.897, indicating strong relationships between indicators and their respective constructs. No indicators required 
removal due to insufficient loading values, confirming the appropriateness of the measurement items selected for 
this research. 

Convergent validity assessment using average variance extracted (AVE) measures demonstrated adequate 
results for all constructs. AVE values ranged from 0.634 to 0.777, all exceeding the recommended threshold of 
0.50, indicating that each construct explains more than half of the variance in its indicators. These results provide 
evidence of satisfactory convergent validity across all constructs in the measurement model. 
 

Table 2. Discriminant Validity Assessment - Fornell-Larcker Criterion. 

Construct BDAC DFA SCP FS TR MT CI 

BDAC 0.829 
      

DFA 0.657 0.824 
     

SCP 0.623 0.741 0.823 
    

FS 0.234 0.187 0.203 0.881 
   

TR 0.578 0.456 0.487 0.312 0.805 
  

MT 0.289 0.298 0.267 0.145 0.234 0.838 
 

CI 0.367 0.321 0.389 0.198 0.298 0.456 0.817 
Note: Diagonal elements (in bold) represent the square root of AVE; off-diagonal elements represent construct correlations. 

 
Discriminant validity evaluation using the Fornell-Larcker criterion demonstrated satisfactory results, with 

the square root of each construct's AVE exceeding its correlations with other constructs. This criterion confirms 
that each construct shares more variance with its own indicators than with other constructs in the model, 
providing evidence of adequate discriminant validity. 
 

Table 3. Discriminant Validity Assessment - HTMT Ratio. 

Construct BDAC DFA SCP FS TR MT CI 

BDAC 
       

DFA 0.734 
      

SCP 0.701 0.831 
     

FS 0.267 0.214 0.233 
    

TR 0.648 0.521 0.558 0.356 
   

MT 0.334 0.346 0.311 0.181 0.278 
  

CI 0.421 0.371 0.451 0.234 0.345 0.534 
 

 
The heterotrait-monotrait (HTMT) ratio assessment provided additional discriminant validity evaluation, with 

all HTMT values below the conservative threshold of 0.85 for conceptually distinct constructs. The highest 
HTMT value of 0.831 between demand forecasting accuracy and supply chain performance remained below the 
threshold, confirming adequate discriminant validity despite the theoretical relationship between these constructs. 
 

4.2. Structural Model Assessment 
The structural model evaluation focused on assessing the hypothesised relationships and testing the mediating 

role of demand forecasting accuracy in the relationship between big data analytics capabilities and supply chain 
performance. The structural model demonstrated adequate explanatory power, with R² values of 0.432 for demand 
forecasting accuracy and 0.587 for supply chain performance, indicating that the model explains 43.2% and 58.7% 
of the variance in these constructs respectively. 
 

Table 4. Direct Effects Analysis. 

Hypothesis Path Path 
Coefficient 

Standard 
Error 

t-Value p-Value 95% CI Lower 95% CI 
Upper 

Decision 

H1 BDAC → DFA 0.657 0.047 13.978 0.000 0.565 0.749 Supported 

H2 DFA → SCP 0.542 0.051 10.627 0.000 0.442 0.642 Supported 

H3 BDAC → SCP 0.267 0.059 4.525 0.000 0.151 0.383 Supported 

 
The direct effects analysis revealed significant positive relationships for all hypothesised paths. Big data 

analytics capabilities demonstrated a strong positive effect on demand forecasting accuracy (β = 0.657, p < 0.001), 

supporting H1. Demand forecasting accuracy showed a significant positive effect on supply chain performance (β = 
0.542, p < 0.001), supporting H2. The direct effect of big data analytics capabilities on supply chain performance 

was also significant (β = 0.267, p < 0.001), supporting H3. 



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Bootstrap analysis with 5,000 resamples confirmed the statistical significance of all direct effects, with 
confidence intervals excluding zero for all path coefficients. The effect sizes, as assessed through Cohen's f², 
indicated medium to large effects for the relationships between big data analytics capabilities and demand 
forecasting accuracy (f² = 0.761) and between demand forecasting accuracy and supply chain performance (f² = 
0.417), whilst the direct effect of big data analytics capabilities on supply chain performance showed a small to 
medium effect size (f² = 0.097). 
 

Table 5. Predictive Relevance Assessment. 

Construct R² R² Adjusted Q² Effect Size (f²) 

Demand Forecasting Accuracy 0.432 0.430 0.287 - 
Supply Chain Performance 0.587 0.582 0.391 - 

BDAC → DFA - - - 0.761 

DFA → SCP - - - 0.417 

BDAC → SCP - - - 0.097 

 
The predictive relevance assessment using Stone-Geisser's Q² statistic yielded positive values for both 

endogenous constructs (Q² = 0.287 for demand forecasting accuracy and Q² = 0.391 for supply chain performance), 
confirming the model's predictive relevance. These results indicate that the model possesses predictive capability 
beyond what would be expected by chance, supporting the practical utility of the proposed theoretical framework. 
 

Table 6. Specific Indirect Effects - Mediation Analysis. 

Mediation Path Indirect 
Effect 

Standard 
Error 

t-Value p-Value 95% CI 
Lower 

95% CI 
Upper 

VAF Mediation Type 

BDAC → DFA → SCP 0.356 0.041 8.683 0.000 0.276 0.436 0.571 Partial Mediation 

 
The mediation analysis revealed a significant indirect effect of big data analytics capabilities on supply chain 

performance through demand forecasting accuracy (β = 0.356, p < 0.001). The variance accounted for (VAF) of 
0.571 indicates that approximately 57.1% of the total effect of big data analytics capabilities on supply chain 
performance is mediated through demand forecasting accuracy, whilst 42.9% represents the direct effect. This 
pattern confirms partial mediation, supporting H4. 

The total effect of big data analytics capabilities on supply chain performance was 0.623 (direct effect: 0.267 + 
indirect effect: 0.356), indicating a substantial overall relationship. The significant indirect effect demonstrates that 
demand forecasting accuracy serves as an important mechanism through which analytics capabilities translate into 
supply chain performance improvements. 
 

Table 7. Control Variables Effects. 

Control Variable Path to DFA Path to SCP 

Firm Size 0.089* 0.112** 
Technology Readiness 0.156*** 0.134** 

Market Turbulence -0.087* -0.098* 
Competitive Intensity 0.067 0.089* 

Note: *p < 0.05, **p < 0.01, ***p < 0.001. 

 
The control variables demonstrated varying effects on the endogenous constructs. Technology readiness 

showed significant positive effects on both demand forecasting accuracy and supply chain performance, whilst 
market turbulence exhibited negative effects on both constructs. Firm size demonstrated positive effects on both 
constructs, whilst competitive intensity showed a significant effect only on supply chain performance. 
 

4.3. Supplementary Analyses 
Multi-group analysis (MGA) was conducted to examine potential differences in the proposed relationships 

across organisational subgroups. The analysis focused on three grouping variables: firm size (small vs. large), 
geographic region (northern vs. southern Vietnam), and e-commerce platform type (own website vs. marketplace 
platforms). 

 
Table 8. Multi-Group Analysis Results 

Structural Path Firm Size Comparison 

Small Firms 
(n = 143) 

Large Firms 
(n = 144) 

Path Difference 
|Small - Large| 

p-Value 

BDAC → DFA 0.634 0.681 0.047 0.367 

DFA → SCP 0.578 0.506 0.072 0.142 

BDAC → SCP 0.289 0.245 0.044 0.394  
Northern Vietnam 

(n = 134) 
Southern Vietnam 

(n = 153) 
  

BDAC → DFA 0.672 0.643 0.029 0.456 

DFA → SCP 0.521 0.563 0.042 0.378 

BDAC → SCP 0.251 0.283 0.032 0.412 

 
The multi-group analysis revealed no significant differences in path coefficients across the examined subgroups 

(all p-values > 0.05), suggesting that the proposed relationships are consistent across different organisational sizes, 
geographic regions, and platform types. This finding enhances the generalisability of the results across Vietnam's 
diverse fashion e-commerce landscape. 
 

 



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Table 9. FsQCA Analysis - Configurations for High Supply Chain Performance. 

Configuration BDAC DFA TR Consistency Raw Coverage Unique Coverage 
Config 1 ● ● ● 0.867 0.423 0.134 

Config 2 ● ● ○ 0.834 0.387 0.098 

Config 3 ○ ● ● 0.812 0.267 0.087 
Note: ● = presence of condition, ○ = absence of condition, blank = don't care condition. 

 
The fuzzy-set qualitative comparative analysis (fsQCA) identified three distinct configurations leading to high 

supply chain performance. Configuration 1, characterised by the presence of high big data analytics capabilities, 
high demand forecasting accuracy, and high technology readiness, demonstrated the highest consistency (0.867) 
and raw coverage (0.423). This configuration represents the most effective pathway to superior supply chain 
performance. 

Configuration 2, involving high big data analytics capabilities and high demand forecasting accuracy but low 
technology readiness, showed moderate consistency (0.834) and coverage (0.387). This finding suggests that 
organisations can achieve good performance even with limited technology readiness if they possess strong analytics 
capabilities and forecasting accuracy. 

Configuration 3, characterised by low big data analytics capabilities but high demand forecasting accuracy and 
high technology readiness, demonstrated adequate consistency (0.812) but lower coverage (0.267). This 
configuration indicates that alternative pathways to performance exist, emphasising the importance of forecasting 
accuracy even in the absence of advanced analytics capabilities. 
 

Table 10. Simple Slope Analysis - Moderating Effects. 

Moderator Level BDAC → DFA DFA → SCP 
High Technology Readiness (+1 SD) 0.721 0.598 
Mean Technology Readiness 0.657 0.542 
Low Technology Readiness (-1 SD) 0.593 0.486 
Slope Difference 0.128 0.112 
Significance of Moderation p < 0.05 p < 0.05 

 
The simple slope analysis revealed significant moderating effects of technology readiness on both the 

relationship between big data analytics capabilities and demand forecasting accuracy, and between demand 
forecasting accuracy and supply chain performance. Organisations with high technology readiness demonstrated 

stronger relationships between analytics capabilities and forecasting accuracy (β = 0.721) compared to those with 

low technology readiness (β = 0.593). 
Similarly, the relationship between demand forecasting accuracy and supply chain performance was stronger 

for organisations with high technology readiness (β = 0.598) compared to those with low technology readiness (β 
= 0.486). These findings suggest that technology readiness serves as an important contingency factor that 
enhances the effectiveness of both analytics capabilities and forecasting accuracy in generating performance 
outcomes. 
 

5. Discussion of Research Results and Conclusions 

The empirical findings of this research provide compelling evidence for the mediating role of demand 
forecasting accuracy in the relationship between big data analytics capabilities and supply chain performance within 
Vietnam's fashion e-commerce ecosystem. These results offer significant theoretical contributions to the emerging 
literature on digital transformation in supply chain management whilst providing practical insights for fashion e-
commerce enterprises seeking to optimise their operational performance through enhanced analytical capabilities. 

The strong positive relationship between big data analytics capabilities and demand forecasting accuracy (β = 
0.657, p < 0.001) aligns with theoretical expectations derived from resource-based view theory and empirical 
findings from previous research (Chen et al., 2017; Wamba et al., 2017). This finding demonstrates that 
organisations investing in comprehensive analytics capabilities, encompassing technical infrastructure, analytical 
talent, and data-driven culture, achieve substantially improved forecasting accuracy compared to those with limited 
analytical resources. The effect size (f² = 0.761) indicates a large practical significance, suggesting that analytics 
capability development represents a critical strategic priority for fashion e-commerce enterprises. 

The significant relationship between demand forecasting accuracy and supply chain performance (β = 0.542, p 
< 0.001) corroborates established supply chain management literature emphasising the importance of accurate 
demand prediction for operational efficiency (Syntetos et al., 2016; Gunasekaran et al., 2017). The medium to large 
effect size (f² = 0.417) underscores the practical importance of forecasting accuracy for achieving superior supply 
chain outcomes. Within the context of Vietnam's fashion e-commerce sector, this finding suggests that 
organisations achieving forecasting accuracy improvements can expect substantial reductions in stockout incidents, 
enhanced customer satisfaction, and improved financial performance. 

The partial mediation finding represents a particularly important theoretical contribution, demonstrating that 
demand forecasting accuracy accounts for approximately 57.1% of the total effect of big data analytics capabilities 
on supply chain performance. This result supports the theoretical proposition that analytics capabilities must 
translate into specific operational improvements to generate tangible performance benefits. The remaining direct 
effect (42.9%) suggests that analytics capabilities also influence supply chain performance through alternative 
mechanisms beyond forecasting accuracy, potentially including real-time decision-making capabilities, supplier 
relationship management, and customer service enhancements. 

The mediating role of demand forecasting accuracy provides empirical support for dynamic capabilities theory's 
emphasis on sensing, seizing, and reconfiguring processes (Teece, 2007). The sensing dimension is manifested 
through the ability of analytics capabilities to identify patterns and trends within complex datasets, which 
translates into improved forecasting accuracy. The seizing dimension emerges through the translation of 



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forecasting insights into operational decisions that enhance supply chain performance. The reconfiguring 
dimension is reflected in the continuous adaptation and improvement of forecasting models based on performance 
feedback and changing market conditions. 

The findings regarding the multidimensional nature of big data analytics capabilities offer important insights 
for both theory and practice. The research demonstrates that technical infrastructure, analytical talent, and data-
driven culture collectively contribute to overall analytics capabilities, with each dimension playing a distinct role in 
forecasting accuracy improvement. This finding aligns with the resource-based view's emphasis on the 
complementary nature of strategic resources and the importance of resource orchestration for competitive 
advantage generation (Barney, 1991; Wernerfelt, 1984). 

The control variable effects provide additional insights into the contextual factors influencing the proposed 
relationships within Vietnam's fashion e-commerce sector. Technology readiness emerges as a particularly 
important factor, demonstrating significant positive effects on both demand forecasting accuracy and supply chain 
performance. This finding suggests that organisations' overall technological sophistication enhances their ability to 
leverage analytics capabilities effectively, supporting the importance of comprehensive digital transformation 
initiatives rather than isolated analytics investments. 

The moderating effect of technology readiness revealed through supplementary analyses provides further 
evidence of the contingent nature of analytics capability effectiveness. Organisations with high technology 
readiness demonstrate stronger relationships between analytics capabilities and performance outcomes, suggesting 
that contextual factors significantly influence the value derived from analytics investments. This finding has 
important implications for emerging market enterprises that may face technological infrastructure constraints. 

The fuzzy-set qualitative comparative analysis (fsQCA) results offer valuable insights into alternative pathways 
to superior supply chain performance. The identification of three distinct configurations demonstrates that multiple 
routes to performance excellence exist, with different combinations of analytics capabilities, forecasting accuracy, 
and technology readiness leading to successful outcomes. Configuration 1, characterised by high levels of all three 
conditions, represents the optimal pathway but may be challenging for resource-constrained organisations to 
achieve simultaneously. 

Configuration 2's success despite low technology readiness suggests that organisations can compensate for 
technological limitations through superior analytics capabilities and forecasting accuracy. This finding is 
particularly relevant for emerging market contexts where technological infrastructure may lag developed market 
standards. Configuration 3's effectiveness despite low analytics capabilities indicates that organisations can achieve 
performance improvements through alternative approaches emphasising forecasting accuracy and technology 
readiness. 

The multi-group analysis results demonstrating consistency across organisational sizes, geographic regions, 
and platform types enhance the generalisability of the findings across Vietnam's diverse fashion e-commerce 
landscape. This consistency suggests that the proposed relationships are robust across different operational 
contexts and organisational characteristics, supporting the theoretical validity of the research model. 

The research findings have important implications for fashion e-commerce enterprises seeking to enhance their 
supply chain performance through digital transformation initiatives. The results suggest that analytics capability 
development should be approached comprehensively, encompassing technical infrastructure investments, talent 
development programmes, and cultural transformation initiatives. Organisations focusing exclusively on 
technological solutions without addressing human capital and cultural dimensions may achieve suboptimal returns 
on their analytics investments. 

The mediating role of demand forecasting accuracy highlights the importance of translating analytics 
capabilities into specific operational improvements. Organisations should establish clear metrics for forecasting 
accuracy and implement systematic processes for incorporating analytical insights into demand planning activities. 
The development of forecasting capabilities should be prioritised as a critical link between analytics investments 
and performance outcomes. 

The moderating effect of technology readiness suggests that organisations should assess their overall 
technological infrastructure before implementing advanced analytics capabilities. Investments in foundational 
technologies, including enterprise resource planning systems, data management platforms, and network 
infrastructure, may be necessary precursors to successful analytics capability development. 

The research contributes to the theoretical understanding of digital transformation in supply chain 
management by providing empirical evidence of the mechanisms through which analytics capabilities influence 
performance outcomes. The identification of demand forecasting accuracy as a critical mediating variable advances 
theoretical knowledge about the operational pathways through which digital technologies generate competitive 
advantage. The research also contributes to the emerging literature on analytics capabilities in emerging markets 
by demonstrating the relevance of established theoretical frameworks within developing economy contexts. 

Several limitations should be acknowledged when interpreting these research findings. The cross-sectional 
design precludes causal inferences about the directionality of the relationships, although the theoretical foundation 
provides strong support for the proposed causal ordering. Longitudinal research examining the development of 
analytics capabilities and their performance effects over time would provide additional insights into the dynamic 
nature of these relationships. The focus on Vietnam's fashion e-commerce sector, whilst providing contextual 
depth, may limit the generalisability of findings to other industries or geographic contexts. 

Future research opportunities emerge from these findings and limitations. Longitudinal studies examining the 
temporal development of analytics capabilities and their performance effects would provide valuable insights into 
the dynamic processes underlying digital transformation. Cross-cultural research comparing emerging and 
developed markets would enhance understanding of contextual factors influencing analytics capability 
effectiveness. Industry-specific studies examining the applicability of these findings across different sectors would 
contribute to theoretical generalisation. 

The exploration of additional mediating mechanisms beyond demand forecasting accuracy represents another 
important research direction. Analytics capabilities may influence supply chain performance through various 



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pathways, including supplier relationship management, inventory optimisation, and customer service enhancement. 
Understanding these alternative mechanisms would provide a more comprehensive view of how analytics 
capabilities generate competitive advantage. 

Research examining the role of artificial intelligence and machine learning technologies in enhancing analytics 
capabilities would address the rapidly evolving technological landscape. The integration of emerging technologies 
such as blockchain, Internet of Things, and edge computing with analytics capabilities presents opportunities for 
further performance enhancement that warrant systematic investigation. 

In conclusion, this research provides empirical evidence supporting the critical role of demand forecasting 
accuracy as a mediating mechanism between big data analytics capabilities and supply chain performance within 
Vietnam's fashion e-commerce sector. The findings demonstrate the importance of comprehensive analytics 
capability development, encompassing technical, human, and cultural dimensions, for achieving superior 
operational performance. The research contributes to both theoretical understanding and practical application by 
illuminating the pathways through which digital transformation initiatives generate competitive advantage in 
emerging market contexts. These insights provide valuable guidance for fashion e-commerce enterprises, 
technology providers, and policymakers seeking to enhance supply chain performance through strategic analytics 
capability development. 
 

Acknowledgments: 
I would like to express my sincere gratitude to Dr. Hoang Vu Hiep for his invaluable guidance and inspiration 
throughout this research. His expertise, insights, and unwavering support have been instrumental in shaping the 
direction and quality of this study. I am deeply appreciative of his generosity in sharing his time, knowledge, and 
network, which have greatly contributed to the success of this research. His mentorship and commitment to 
academic excellence have not only enriched the quality of this work but have also had a profound impact on my 
personal and professional growth. 
 

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