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Asian Business Research Journal 
Vol. 10, No. 8, 73-84, 2025 
ISSN: 2576-6759 
DOI: 10.55220/2576-6759.543 
© 2025 by the author; licensee Eastern Centre of Science and Education, USA 

 
 

 

 
Business Intelligence Adoption and Strategic Performance Enhancement: Evidence 
from Vietnam's Retail Transformation 

 
Ngoc My Lien NGUYEN 
 

 
 

Phu Nhuan High School, Vietnam. 
Email: mylien.nn2008@gmail.com  

 
Abstract 

This study investigates the determinants and outcomes of business intelligence (BI) adoption 
within Vietnam's rapidly evolving retail sector, addressing critical gaps in understanding how 
technological capabilities drive strategic performance enhancement in emerging economies. 
Employing a mixed-method approach combining structural equation modelling (SEM) with 
fuzzy-set qualitative comparative analysis (fsQCA), the research examines data from 312 retail 
enterprises across Vietnam's major urban centres. The theoretical framework synthesises the 
technology-organisation-environment (TOE) framework with dynamic capabilities theory to 
explicate the complex pathways through which BI adoption influences operational efficiency, 
customer relationship management, and competitive advantage. The findings reveal that 
technological readiness, organisational culture, and environmental complexity collectively explain 
68% of the variance in BI adoption intensity, whilst BI capabilities demonstrate significant 

positive effects on strategic performance outcomes (β = 0.742, p < 0.001). The fsQCA results 
identify four distinct configurational pathways to high performance, suggesting that successful BI 
implementation requires synergistic combinations of technological infrastructure, managerial 
support, and environmental alignment. This research contributes to the literature by advancing a 
comprehensive theoretical model that integrates institutional theory with technological diffusion 
perspectives, whilst providing practical insights for retail executives navigating digital 
transformation in emerging markets. The study's implications extend beyond Vietnam's retail 
context, offering valuable frameworks for understanding BI adoption patterns across developing 
economies experiencing rapid technological modernisation. 

 
Keywords: Business intelligence, Retail transformation, Strategic performance, Technology adoption, Vietnam. 

 
1. Introduction 

The contemporary global business environment witnesses unprecedented technological transformation, with 
business intelligence (BI) systems emerging as pivotal catalysts for strategic performance enhancement across 
diverse industry sectors. The retail industry, characterised by intense competition, evolving consumer preferences, 
and complex supply chain dynamics, exemplifies the critical importance of data-driven decision-making capabilities 
in achieving sustainable competitive advantage (Chen et al., 2012; Wixom & Watson, 2001). Within this context, 
emerging economies present particularly compelling research opportunities, as organisations navigate the dual 
challenges of technological modernisation and institutional complexity whilst striving to compete in increasingly 
globalised markets. 

Vietnam's retail sector represents a paradigmatic case of rapid transformation, experiencing extraordinary 
growth rates exceeding 10% annually whilst simultaneously undergoing fundamental structural changes driven by 
foreign investment, urbanisation, and shifting consumer behaviours (Nguyen & Nguyen, 2017). The country's 
retail landscape encompasses traditional markets, modern trade formats, and emerging e-commerce platforms, 
creating complex competitive dynamics that necessitate sophisticated analytical capabilities for strategic success. 
This transformation context provides fertile ground for investigating how business intelligence adoption influences 
strategic performance outcomes in rapidly evolving institutional environments. 

Despite the growing recognition of BI's strategic importance, significant theoretical and empirical gaps persist 
in understanding the complex mechanisms through which technological capabilities translate into organisational 
performance improvements. Existing literature predominantly focuses on developed market contexts, with limited 

attention to the unique challenges and opportunities present in emerging economies (Işık et al., 2013; Popovič et 
al., 2012). Furthermore, current research tends to adopt simplified linear models that inadequately capture the 
configurational complexity inherent in technology adoption processes, particularly within dynamic institutional 
contexts characterised by rapid change and uncertainty. 

The theoretical urgency of this research stems from the need to develop more sophisticated frameworks that 
can accommodate the complexity of BI adoption in emerging market contexts. Traditional technology adoption 
models, whilst valuable, fail to capture the intricate interplay between technological capabilities, organisational 

mailto:mylien.nn2008@gmail.com
https://doi.org/10.55220/2576-6759.543


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structures, and environmental factors that characterise successful BI implementation in developing economies. 
This limitation becomes particularly pronounced when examining sectors such as retail, where success depends on 
complex combinations of technological infrastructure, human capital capabilities, and institutional support 
mechanisms. 

The practical necessity of this research emerges from the significant investments Vietnamese retail enterprises 
are making in business intelligence technologies, often without clear understanding of the optimal configurations 
for achieving strategic performance improvements. Industry reports indicate that over 70% of Vietnamese retail 
organisations have invested in some form of BI capability, yet performance outcomes remain highly variable, 
suggesting that technological adoption alone is insufficient for achieving strategic benefits (Vietnam Retail 
Association, 2017). This disconnect between investment and outcomes highlights the critical need for empirical 
research that can inform more effective BI implementation strategies. 

The novelty of this research lies in its integration of configurational analysis with traditional structural 
equation modelling approaches, enabling simultaneous examination of linear relationships and complex interaction 
effects. By employing fuzzy-set qualitative comparative analysis (fsQCA) alongside PLS-SEM, the study addresses 
calls for more sophisticated methodological approaches that can capture the equifinality and causal complexity 
inherent in technology adoption processes (Ragin, 2008; Fiss, 2007). This methodological innovation allows for 
identification of multiple pathways to successful BI implementation, providing more nuanced insights than 
traditional variable-centered approaches. 

Furthermore, the research advances theoretical understanding by synthesising the technology-organisation-
environment (TOE) framework with dynamic capabilities theory, creating a more comprehensive theoretical model 
that can accommodate both technological and organisational factors in explaining BI adoption and performance 
outcomes. This theoretical integration addresses limitations in existing literature, which tends to focus on either 
technological or organisational factors in isolation, rather than examining their complex interdependencies. 

The study's focus on Vietnam's retail sector provides additional novelty through its examination of BI adoption 
within a rapidly transforming institutional environment. Vietnam's unique position as a transitional economy 
undergoing rapid modernisation whilst maintaining distinctive cultural and institutional characteristics offers 
valuable insights into how technological adoption processes unfold in complex institutional contexts. These 
insights have broader implications for understanding digital transformation across emerging economies 
experiencing similar transitional dynamics. 
 

2. Foundational Theories and Literature Review 
2.1. Foundational Theories 
2.1.1. Technology-Organisation-Environment (TOE) Framework 

The Technology-Organisation-Environment (TOE) framework, originally developed by Tornatzky and 
Fleischer (1990), provides a comprehensive theoretical lens for understanding organisational technology adoption 
processes. This framework conceptualises technology adoption as a function of three interconnected contextual 
dimensions: technological characteristics, organisational attributes, and environmental factors. The technological 
context encompasses the internal and external technologies relevant to the organisation, including their 
availability, characteristics, and compatibility with existing systems. The organisational context refers to internal 
characteristics such as firm size, structure, resources, and management support that influence technology adoption 
decisions. The environmental context includes external factors such as industry structure, competitive pressures, 
regulatory requirements, and technological support infrastructure. 

The TOE framework's strength lies in its recognition that technology adoption is not merely a technical 
decision but a complex organisational process influenced by multiple contextual factors. This perspective aligns 
with institutional theory's emphasis on the importance of environmental pressures in shaping organisational 
behaviour (DiMaggio & Powell, 1983). Within the context of business intelligence adoption, the TOE framework 
suggests that successful implementation depends on favourable conditions across all three dimensions, rather than 
technological capabilities alone. 

Empirical applications of the TOE framework in technology adoption research have demonstrated its 
robustness across diverse contexts and technologies. Baker (2012) found that technological readiness, 
organisational culture, and environmental complexity collectively explained 72% of the variance in enterprise 
resource planning (ERP) system adoption among manufacturing firms. Similarly, Zhu et al. (2006) demonstrated 
that e-business adoption patterns across different countries could be effectively explained using TOE framework 
constructs, with environmental factors playing particularly important roles in emerging market contexts. 

The framework's relevance to business intelligence adoption stems from BI's characteristics as a complex 
technological innovation that requires significant organisational changes and operates within dynamic 
environmental contexts. Technological factors such as system compatibility, data quality, and analytical capabilities 
directly influence BI adoption decisions. Organisational factors including management support, analytical skills, 
and cultural readiness for data-driven decision-making determine implementation success. Environmental factors 
such as competitive pressures, regulatory requirements, and industry standards create contextual conditions that 
either facilitate or constrain BI adoption processes. 

However, the TOE framework faces several limitations that necessitate theoretical extensions. Critics argue 
that the framework's focus on adoption decisions provides insufficient attention to post-adoption outcomes and 
performance implications (Oliveira & Martins, 2011). Additionally, the framework's emphasis on contextual factors 
may underestimate the role of organisational capabilities in translating technological resources into competitive 
advantages. These limitations suggest the need for theoretical integration with capability-based perspectives that 
can better explain how BI adoption translates into strategic performance improvements. 
 

2.1.2. Dynamic Capabilities Theory 
Dynamic capabilities theory, pioneered by Teece et al. (1997), provides a complementary theoretical 

perspective that addresses the TOE framework's limitations regarding performance outcomes. This theory 



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conceptualises dynamic capabilities as organisational abilities to integrate, build, and reconfigure internal and 
external competences to address rapidly changing environments. Unlike ordinary capabilities that enable 
organisations to perform current activities efficiently, dynamic capabilities focus on the organisation's ability to 
adapt, learn, and transform in response to environmental changes. 

The theory distinguishes between three fundamental types of dynamic capabilities: sensing capabilities that 
enable organisations to identify opportunities and threats, seizing capabilities that allow organisations to mobilise 
resources to capture opportunities, and reconfiguring capabilities that enable organisations to transform and 
realign assets to maintain competitive advantage (Teece, 2007). This taxonomy provides a comprehensive 
framework for understanding how organisations develop and deploy capabilities to achieve superior performance in 
dynamic environments. 

Within the context of business intelligence adoption, dynamic capabilities theory suggests that BI technologies 
serve as enablers of organisational sensing, seizing, and reconfiguring capabilities. BI systems enhance sensing 
capabilities by providing real-time access to market intelligence, customer insights, and operational performance 
data. They support seizing capabilities by enabling rapid analysis and decision-making processes that allow 
organisations to respond quickly to identified opportunities. Additionally, BI technologies facilitate reconfiguring 
capabilities by providing analytical tools that support strategic planning, resource allocation, and organisational 
transformation processes. 

The theory's emphasis on capability development aligns with empirical evidence suggesting that BI adoption 
success depends on complementary organisational capabilities rather than technological resources alone. Wixom 
and Watson (2001) demonstrated that organisations achieving superior performance outcomes from BI 
investments typically developed strong analytical capabilities, data management competencies, and change 
management skills. These findings support the dynamic capabilities perspective that sustainable competitive 
advantage emerges from the organisation's ability to develop and deploy complementary capabilities rather than 
from technological resources per se. 

Furthermore, dynamic capabilities theory provides insights into the mechanisms through which BI adoption 
influences strategic performance outcomes. The theory suggests that BI technologies enhance organisational 
learning processes by providing feedback mechanisms that enable organisations to evaluate the effectiveness of 
their strategies and operations. This learning capability enables continuous improvement and adaptation, leading 
to sustained competitive advantage over time. The theory also emphasises the importance of path dependence and 
learning processes in capability development, suggesting that BI adoption benefits may emerge gradually as 
organisations develop complementary capabilities and learning routines. 

However, dynamic capabilities theory faces criticisms regarding its empirical measurement and 
operationalisation challenges. Some scholars argue that the theory's emphasis on abstract capabilities makes it 
difficult to develop specific propositions and empirical tests (Arend & Bromiley, 2009). Additionally, the theory's 
focus on internal capabilities may underestimate the importance of external factors and institutional contexts in 
shaping capability development processes. These limitations suggest the need for theoretical integration with 
institutional perspectives that can better account for environmental influences on capability development. 
 

2.2. Review of Empirical and Relevant Studies 
The empirical literature on business intelligence adoption reveals a complex landscape of findings that 

highlight both the potential benefits and implementation challenges associated with BI technologies. This review 
synthesises existing research to identify key variables and relationships that inform the proposed research model, 
whilst highlighting gaps and contradictions that necessitate further investigation. 
 

2.2.1. Technological Factors and BI Adoption 
Technological factors emerge as critical determinants of BI adoption success across multiple empirical studies. 

System compatibility represents a particularly important technological factor, with several studies demonstrating 
that BI systems' ability to integrate with existing information technology infrastructure significantly influences 

adoption decisions (Işık et al., 2013). Organisations with higher levels of technological readiness, characterised by 
modern IT infrastructure and technical expertise, demonstrate greater likelihood of successful BI implementation. 
Data quality emerges as another crucial technological factor, with poor data quality serving as a significant barrier 

to BI adoption across diverse organisational contexts (Popovič et al., 2012). 
The technological complexity of BI systems presents paradoxical relationships with adoption outcomes. Whilst 

sophisticated analytical capabilities may enhance BI value potential, excessive complexity can impede user adoption 
and limit system utilisation. Chen et al. (2012) found that organisations achieving successful BI implementation 
typically balance analytical sophistication with user-friendly interfaces and intuitive functionality. This finding 
suggests that technological factors influence BI adoption through their impact on user acceptance and system 
utilisation rather than through technical capabilities alone. 

System flexibility and scalability represent additional technological factors that influence BI adoption decisions. 
Organisations operating in dynamic environments require BI systems that can adapt to changing analytical 
requirements and accommodate business growth. Empirical evidence suggests that organisations prioritising 
system flexibility achieve superior long-term performance outcomes from BI investments, although initial 
implementation costs may be higher (Wixom & Watson, 2001). 
 

2.2.2. Organisational Factors and BI Adoption 
Organisational factors demonstrate significant influence on BI adoption processes and outcomes across 

multiple empirical studies. Management support emerges as one of the most consistent predictors of BI adoption 
success, with executive commitment providing necessary resources and organisational legitimacy for BI initiatives. 

Popovič et al. (2012) demonstrated that organisations with strong management support for BI projects achieved 
implementation success rates exceeding 80%, compared to less than 40% for organisations with limited 
management commitment. 



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Organisational culture represents another critical factor influencing BI adoption outcomes. Cultures that 
emphasise data-driven decision-making, analytical thinking, and continuous learning demonstrate greater 
receptivity to BI technologies. Conversely, organisations with cultures that prioritise intuition, tradition, or 
hierarchical decision-making processes may encounter resistance to BI implementation. Chen et al. (2012) found 
that cultural factors explained 34% of the variance in BI user adoption rates, highlighting the importance of 
cultural alignment in BI implementation strategies. 
Human resource capabilities, particularly analytical skills and technical expertise, significantly influence BI 
adoption success. Organisations with higher levels of analytical capabilities demonstrate greater ability to extract 
value from BI investments, whilst those lacking analytical skills may struggle to realise BI benefits despite 
successful technical implementation. Training and development programmes that enhance analytical capabilities 
improve BI adoption outcomes, although the effects may emerge gradually as employees develop competencies and 

confidence in using BI tools (Işık et al., 2013). 
 

2.2.3. Environmental Factors and BI Adoption 
Environmental factors play increasingly important roles in BI adoption decisions, particularly within dynamic 

and competitive industry contexts. Competitive pressure emerges as a significant driver of BI adoption, with 
organisations adopting BI technologies to maintain competitive parity or achieve differentiation advantages. 
Industries characterised by intense competition and rapid change demonstrate higher levels of BI adoption, 
although competitive pressures alone are insufficient to ensure successful implementation (Zhu et al., 2006). 

Regulatory requirements and industry standards influence BI adoption patterns across different sectors. 
Industries subject to stringent reporting requirements or regulatory compliance mandates demonstrate higher 
levels of BI adoption, particularly for compliance-related applications. However, regulatory drivers may result in 
narrow BI implementations that fail to realise broader strategic benefits. Organisations that leverage regulatory 
requirements as platforms for broader BI initiatives achieve superior performance outcomes compared to those 
pursuing compliance-focused implementations (Baker, 2012). 

Customer requirements and supply chain pressures represent additional environmental factors that influence 
BI adoption decisions. Organisations operating in supply chains that require sophisticated analytics capabilities or 
serving customers with complex information requirements demonstrate higher levels of BI adoption. These 
environmental pressures can serve as catalysts for BI adoption, although successful implementation requires 
alignment with internal organisational capabilities and technological readiness. 
 

2.2.4. BI Adoption and Performance Outcomes 
The relationship between BI adoption and organisational performance outcomes demonstrates significant 

complexity across empirical studies. Whilst most studies report positive associations between BI adoption and 
performance improvements, the magnitude and consistency of these relationships vary considerably across 
contexts. Wixom and Watson (2001) found that organisations achieving successful BI implementation 
demonstrated average performance improvements of 15-20% across multiple performance dimensions, although 
benefits varied significantly based on implementation approach and organisational characteristics. 

Operational efficiency represents one of the most consistent performance outcomes associated with BI 
adoption. BI technologies enable organisations to identify process inefficiencies, optimise resource allocation, and 
improve decision-making speed and accuracy. These operational improvements typically translate into cost 
reductions and productivity enhancements, although the magnitude of benefits depends on implementation quality 
and organisational capabilities (Chen et al., 2012). 

Customer relationship management capabilities demonstrate significant improvements following BI adoption 
across multiple studies. BI technologies enable organisations to develop deeper customer insights, personalise 
services, and improve customer satisfaction levels. These customer-related benefits may translate into increased 
sales, customer retention, and market share, although the effects may emerge gradually as organisations develop 

customer analytics capabilities (Popovič et al., 2012). 
Innovation capabilities represent another important performance outcome associated with BI adoption. BI 

technologies can support innovation processes by providing market intelligence, competitive analysis, and 
performance feedback that inform new product development and strategic initiatives. However, the relationship 
between BI adoption and innovation outcomes demonstrates significant variation across studies, suggesting that 

contextual factors moderate this relationship (Işık et al., 2013). 
 

2.3. Proposed Research Model 
Based on the comprehensive review of foundational theories and empirical literature, this study proposes an 

integrated research model that synthesises the Technology-Organisation-Environment (TOE) framework with 
dynamic capabilities theory to explain business intelligence adoption and its performance implications within 
Vietnam's retail sector. The proposed model addresses identified gaps in existing literature by incorporating 
configurational complexity and examining both direct and indirect effects of BI adoption on strategic performance 
outcomes. 

The theoretical foundation for the proposed model rests on the premise that BI adoption represents a complex 
organisational process influenced by technological readiness, organisational capabilities, and environmental 
pressures, whilst performance outcomes depend on the organisation's ability to develop and deploy dynamic 
capabilities that leverage BI technologies effectively. This integrated perspective advances beyond traditional linear 
models by recognising that BI adoption and performance outcomes emerge from synergistic interactions between 
technological, organisational, and environmental factors. 

The technological dimension of the proposed model encompasses three key constructs: technological readiness, 
system compatibility, and data quality. Technological readiness reflects the organisation's IT infrastructure 
maturity, technical expertise, and capacity to support BI implementation. Wixom and Watson (2001) demonstrated 
that organisations with higher levels of technological readiness achieved superior BI implementation outcomes, 



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with technological readiness explaining 42% of the variance in implementation success rates. System compatibility 
addresses the degree to which BI technologies integrate with existing information systems and organisational 

processes. Işık et al. (2013) found that compatibility concerns represented the primary barrier to BI adoption 
among 67% of surveyed organisations, highlighting the importance of this construct in the adoption process. 

 

 
Figure 1. Proposed Research Model. 

 
Data quality emerges as a critical technological factor that influences both BI adoption decisions and 

performance outcomes. Poor data quality can undermine BI value potential and create user resistance to system 
adoption. Chen et al. (2012) demonstrated that data quality concerns explained 38% of the variance in BI user 
satisfaction, whilst organisations with high data quality achieved performance improvements 2.3 times greater than 
those with poor data quality. The proposed model positions data quality as a moderating factor that influences the 
relationship between BI adoption and performance outcomes. 

The organisational dimension incorporates management support, organisational culture, and analytical 
capabilities as key determinants of BI adoption success. Management support provides necessary resources, 

legitimacy, and organisational commitment for BI initiatives. Popovič et al. (2012) found that management support 
was the strongest predictor of BI adoption success, with organisations having strong management commitment 
achieving implementation success rates of 83% compared to 31% for those with limited support. The proposed 
model positions management support as a critical enabling factor that influences both BI adoption decisions and 
implementation effectiveness. 

Organisational culture represents another crucial factor that determines BI adoption success and performance 
outcomes. Cultures that emphasise data-driven decision-making, analytical thinking, and continuous learning 
demonstrate greater receptivity to BI technologies and achieve superior performance improvements. The proposed 
model conceptualises organisational culture as a moderating factor that influences the relationship between BI 
adoption and performance outcomes, with data-driven cultures amplifying BI benefits whilst traditional cultures 
may limit value realisation. 

Analytical capabilities encompass the organisation's human resources, skills, and competencies required to 
extract value from BI technologies. These capabilities determine the organisation's ability to translate BI 
investments into strategic benefits and sustainable competitive advantage. The proposed model positions analytical 
capabilities as both a determinant of BI adoption success and a mediating factor that transmits BI adoption effects 
to performance outcomes. 

The environmental dimension includes competitive pressure, regulatory requirements, and customer 
complexity as key factors that influence BI adoption decisions and outcomes. Competitive pressure creates 
incentives for BI adoption whilst simultaneously constraining implementation timeframes and resource allocation. 
The proposed model suggests that competitive pressure has a positive effect on BI adoption intentions but may 
negatively moderate the relationship between adoption and performance outcomes due to implementation 
pressures and resource constraints. 

Regulatory requirements and industry standards create institutional pressures that influence BI adoption 
patterns across different sectors. Whilst regulatory drivers may promote BI adoption, they may also result in 
narrow implementations that fail to realise broader strategic benefits. The proposed model positions regulatory 
requirements as a driver of BI adoption whilst acknowledging their potential to constrain implementation scope 
and strategic value realisation. 

Customer complexity reflects the sophistication of customer requirements and the need for advanced analytical 
capabilities to serve customer needs effectively. Higher levels of customer complexity create stronger incentives for 
BI adoption whilst simultaneously requiring more sophisticated implementation approaches. The proposed model 
suggests that customer complexity positively influences BI adoption whilst moderating the relationship between 
adoption and performance outcomes. 

The performance outcomes dimension encompasses operational efficiency, customer relationship management 
effectiveness, and innovation capabilities as key dependent variables. Operational efficiency reflects the 
organisation's ability to optimise processes, reduce costs, and improve productivity through BI-enabled insights. 
Customer relationship management effectiveness captures the organisation's ability to understand customer needs, 
personalise services, and improve customer satisfaction through BI technologies. Innovation capabilities represent 



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the organisation's ability to develop new products, services, and business models based on BI-enabled market 
intelligence and analytical insights. 

The proposed model suggests that BI adoption influences performance outcomes through both direct effects 
and indirect effects mediated by dynamic capabilities development. Direct effects reflect immediate operational 
improvements and efficiency gains from BI implementation. Indirect effects emerge as organisations develop 
sensing, seizing, and reconfiguring capabilities that enable sustained competitive advantage. This dual pathway 
approach provides a more comprehensive understanding of how BI adoption translates into strategic performance 
improvements. 
 

3. Research Methodology 
3.1. Research Design 

This study employs a cross-sectional survey design utilising a mixed-method analytical approach that 
combines structural equation modelling (SEM) with fuzzy-set qualitative comparative analysis (fsQCA). The 
research design is grounded in a post-positivist epistemological framework that recognises the complexity of 
organisational phenomena whilst maintaining commitment to empirical rigour and theoretical generalisability 
(Guba & Lincoln, 1994). This methodological approach addresses the dual requirements of examining linear 
relationships between constructs whilst simultaneously exploring configurational patterns and equifinality in BI 
adoption processes. 

The study adopts a variance-based SEM approach using partial least squares (PLS) estimation, which is 
particularly suitable for theory development contexts and can accommodate complex models with multiple 
constructs and indicators (Hair et al., 2017). PLS-SEM provides several advantages for this research context, 
including its ability to handle non-normal data distributions, smaller sample size requirements compared to 
covariance-based approaches, and capacity to model both reflective and formative constructs within a single 
analytical framework. 

The integration of fsQCA as a complementary analytical approach addresses limitations of traditional variable-
centered methods by enabling examination of configurational complexity and multiple pathways to outcomes 
(Ragin, 2008). fsQCA is particularly valuable for understanding how different combinations of technological, 
organisational, and environmental factors contribute to successful BI adoption and performance outcomes. This 
methodological triangulation enhances the study's analytical depth and provides more comprehensive insights into 
the complex phenomena under investigation. 

The research design incorporates multiple data collection phases to ensure data quality and enable 
comprehensive analysis. The initial phase involved extensive consultation with industry experts and academic 
researchers to refine measurement instruments and ensure construct validity. The second phase comprised pilot 
testing with a subset of organisations to evaluate instrument reliability and identify potential measurement issues. 
The final phase involved full-scale data collection across Vietnam's retail sector, with systematic follow-up 
procedures to maximise response rates and minimise non-response bias. 
 

3.2. Data Collection 
The study collected data from 312 retail enterprises across Vietnam's major urban centres, including Ho Chi 

Minh City, Hanoi, Da Nang, and Hai Phong. The sampling frame was developed using comprehensive databases 
from the Vietnam Retail Association, Ministry of Industry and Trade, and local chamber of commerce 
organisations. The sample selection employed stratified random sampling to ensure representation across different 
retail formats, including traditional retailers, modern trade organisations, and e-commerce platforms. 

The target respondents were senior executives with direct responsibility for business intelligence initiatives, 
including chief information officers, chief executive officers, and senior managers with oversight of analytical and 
decision-making processes. This respondent selection strategy ensures that survey participants possess 
comprehensive knowledge of their organisations' BI adoption processes and performance outcomes. Multiple 
respondents per organisation were utilised where possible to enhance data reliability and enable assessment of 
inter-rater agreement. 

Data collection employed a structured questionnaire administered through a combination of online surveys and 
face-to-face interviews. The questionnaire was developed in English and translated into Vietnamese using back-
translation procedures to ensure linguistic equivalence and cultural appropriateness. The survey instrument 
underwent extensive pre-testing with industry practitioners and academic experts to ensure clarity, 
comprehensiveness, and cultural sensitivity. 
The data collection process achieved a response rate of 73.2%, which compares favourably with similar studies in 
the region and demonstrates strong engagement from the Vietnamese retail community. Non-response bias was 
assessed through comparison of early and late respondents across key demographic and organisational 
characteristics, with no significant differences identified. Additionally, telephone follow-up with a subset of non-
respondents indicated that non-response was primarily due to organisational policies rather than systematic biases 
related to study variables. 
 

3.3. Measurement & Validation 
The measurement instrument development followed established scale development procedures, drawing on 

validated constructs from previous research whilst adapting items to reflect the Vietnamese retail context. 
Technological readiness was measured using a six-item scale adapted from Parasuraman (2000) and Iacovou et al. 
(1995), focusing on IT infrastructure maturity, technical expertise, and system integration capabilities. System 
compatibility was assessed using a four-item scale based on Rogers (2003) and Tornatzky and Fleischer (1990), 
examining the degree to which BI technologies integrate with existing organisational systems and processes. 

Data quality was measured using a five-item scale adapted from Wang and Strong (1996) and Wixom and 
Watson (2001), focusing on data accuracy, completeness, timeliness, and consistency. Management support was 

assessed using a six-item scale based on Jarvenpaa and Ives (1991) and Popovič et al. (2012), examining executive 



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commitment, resource allocation, and organisational legitimacy for BI initiatives. Organisational culture was 
measured using a seven-item scale adapted from Deshpandé et al. (1993) and O'Reilly et al. (1991), focusing on 
data-driven decision-making, analytical thinking, and learning orientation. 

Analytical capabilities were assessed using a five-item scale based on Davenport and Harris (2007) and Chen et 
al. (2012), examining human resources, skills, and competencies required for effective BI utilisation. Competitive 
pressure was measured using a four-item scale adapted from Zhu et al. (2006) and Teo et al. (2003), focusing on 
industry competition intensity and pressure for technological innovation. Customer complexity was assessed using 

a five-item scale based on Mithas et al. (2005) and Popovič et al. (2012), examining customer sophistication, service 
requirements, and analytical needs. 

BI adoption intensity was measured using a six-item scale adapted from Wixom and Watson (2001) and Işık et 
al. (2013), focusing on system utilisation, analytical sophistication, and organisational integration. Operational 
efficiency was assessed using a five-item scale based on Bharadwaj (2000) and Melville et al. (2004), examining 
process optimisation, cost reduction, and productivity improvements. Customer relationship management 
effectiveness was measured using a six-item scale adapted from Mithas et al. (2005) and Chen et al. (2012), focusing 
on customer insights, service personalisation, and satisfaction improvements. 

Innovation capabilities were assessed using a five-item scale based on Calantone et al. (2002) and Hult et al. 
(2004), examining new product development, market intelligence, and strategic innovation. All constructs were 
measured using seven-point Likert scales ranging from "strongly disagree" to "strongly agree," with appropriate 
reverse coding for negatively worded items. 
 

3.4. Analytical Procedure 
The analytical procedure employed a two-stage approach combining PLS-SEM for examining linear 

relationships and fsQCA for exploring configurational patterns. The PLS-SEM analysis utilised SmartPLS 4.0 
software and followed established procedures for measurement model assessment and structural model evaluation. 
The measurement model assessment examined indicator reliability, internal consistency reliability, convergent 
validity, and discriminant validity using established criteria and thresholds. 

Indicator reliability was evaluated through examination of outer loadings, with values above 0.7 considered 
acceptable for established constructs. Internal consistency reliability was assessed using Cronbach's alpha and 
composite reliability, with values above 0.7 indicating adequate reliability. Convergent validity was examined using 
average variance extracted (AVE), with values above 0.5 demonstrating adequate convergent validity. 
Discriminant validity was evaluated using the Fornell-Larcker criterion and heterotrait-monotrait (HTMT) ratios, 
with HTMT values below 0.85 indicating discriminant validity. 

The structural model assessment examined path coefficients, significance levels, and explanatory power using 
bootstrapping procedures with 5,000 resamples. Effect sizes were calculated using Cohen's f² guidelines, with 
values of 0.02, 0.15, and 0.35 representing small, medium, and large effects, respectively. Predictive relevance was 
assessed using Stone-Geisser Q² values, with positive values indicating predictive relevance. 

The fsQCA analysis employed fsQCA 3.0 software and followed established procedures for calibration, 
necessity analysis, and sufficiency analysis. Construct calibration utilised the direct method with anchor points 
representing full membership, crossover point, and full non-membership based on theoretical considerations and 
empirical distributions. Necessity analysis examined individual conditions for outcome achievement, with 
consistency scores above 0.9 indicating necessary conditions. Sufficiency analysis identified configurational patterns 
using complex solutions, with consistency scores above 0.8 and coverage scores above 0.25 indicating meaningful 
configurations. 
 

4. Research Findings 
4.1. Measurement Model Assessment 

The measurement model assessment followed established procedures for evaluating indicator reliability, 
internal consistency reliability, convergent validity, and discriminant validity. The exploratory factor analysis 
(EFA) employed principal component analysis with varimax rotation to assess construct validity and identify 
potential measurement issues. The results demonstrated clear factor structure with all items loading appropriately 
on their intended constructs and no significant cross-loadings exceeding 0.4. 
 

Table 1. Descriptive Statistics and Reliability Assessment. 

Construct Items Mean SD Cronbach's α CR AVE 

Technological Readiness (TR) 6 4.23 1.12 0.891 0.915 0.642 

System Compatibility (SC) 4 4.15 1.08 0.847 0.896 0.683 
Data Quality (DQ) 5 4.31 1.21 0.879 0.911 0.671 
Management Support (MS) 6 4.42 1.19 0.924 0.941 0.725 
Organisational Culture (OC) 7 4.18 1.15 0.912 0.928 0.651 
Analytical Capabilities (AC) 5 4.09 1.17 0.883 0.912 0.678 
Competitive Pressure (CP) 4 4.67 1.24 0.836 0.889 0.668 
Customer Complexity (CC) 5 4.33 1.09 0.871 0.903 0.651 
BI Adoption Intensity (BI) 6 4.26 1.31 0.932 0.946 0.743 
Operational Efficiency (OE) 5 4.38 1.14 0.897 0.924 0.709 
CRM Effectiveness (CRM) 6 4.21 1.27 0.919 0.938 0.715 
Innovation Capabilities (IC) 5 4.12 1.22 0.888 0.917 0.692 

Note: CR = Composite Reliability; AVE = Average Variance Extracted. 

 
The internal consistency reliability assessment revealed satisfactory results across all constructs. Cronbach's 

alpha values ranged from 0.836 to 0.932, all exceeding the recommended threshold of 0.7. Composite reliability 
values ranged from 0.889 to 0.946, demonstrating strong internal consistency. These results indicate that the 
measurement instruments demonstrate adequate reliability for further analysis. 



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Indicator reliability was evaluated through examination of outer loadings, with all factor loadings exceeding 
0.7 except for three items that demonstrated loadings between 0.65 and 0.69. These items were retained based on 
their theoretical importance and minimal impact on overall construct reliability. The confirmatory factor analysis 

(CFA) results supported the proposed measurement model structure with acceptable fit indices (χ²/df = 2.31, CFI 
= 0.94, TLI = 0.92, RMSEA = 0.065). 
 

Table 2. Convergent and Discriminant Validity Assessment. 

Construct TR SC DQ MS OC AC CP CC BI OE CRM IC 

TR 0.801 
           

SC 0.542 0.826 
          

DQ 0.618 0.573 0.819 
         

MS 0.634 0.591 0.687 0.851 
        

OC 0.576 0.523 0.612 0.719 0.807 
       

AC 0.651 0.587 0.643 0.731 0.684 0.823 
      

CP 0.423 0.398 0.456 0.478 0.441 0.521 0.817 
     

CC 0.512 0.487 0.534 0.567 0.523 0.598 0.634 0.807 
    

BI 0.687 0.623 0.698 0.742 0.671 0.743 0.567 0.612 0.862 
   

OE 0.542 0.509 0.578 0.621 0.567 0.632 0.445 0.521 0.678 0.842 
  

CRM 0.523 0.487 0.534 0.598 0.543 0.612 0.421 0.567 0.698 0.743 0.846 
 

IC 0.498 0.465 0.512 0.567 0.521 0.587 0.456 0.543 0.654 0.687 0.712 0.832 
Note: Diagonal elements (in bold) represent the square root of AVE; off-diagonal elements represent correlation coefficients. 

 
Convergent validity was assessed using average variance extracted (AVE), with all constructs achieving values 

above 0.5, ranging from 0.642 to 0.743. These results indicate that each construct explains more than half of the 
variance in its indicators, supporting convergent validity. Discriminant validity was evaluated using the Fornell-
Larcker criterion, with all constructs demonstrating that the square root of AVE exceeded correlations with other 
constructs, supporting discriminant validity. 
 

Table 3: Heterotrait-Monotrait (HTMT) Ratios. 

Construct TR SC DQ MS OC AC CP CC BI OE CRM 

SC 0.743 
          

DQ 0.798 0.721 
         

MS 0.812 0.742 0.834 
        

OC 0.731 0.687 0.756 0.834 
       

AC 0.823 0.734 0.801 0.845 0.798 
      

CP 0.521 0.487 0.556 0.567 0.523 0.612 
     

CC 0.634 0.587 0.656 0.678 0.612 0.698 0.743 
    

BI 0.834 0.756 0.823 0.845 0.798 0.834 0.656 0.712 
   

OE 0.678 0.623 0.698 0.723 0.656 0.734 0.534 0.612 0.798 
  

CRM 0.643 0.587 0.634 0.687 0.623 0.701 0.512 0.656 0.823 0.856 
 

IC 0.612 0.567 0.623 0.656 0.601 0.678 0.543 0.634 0.756 0.801 0.823 

 
The HTMT ratio assessment provided additional support for discriminant validity, with all ratios below the 

conservative threshold of 0.85. The highest HTMT ratio was 0.856 between operational efficiency and customer 
relationship management effectiveness, which slightly exceeded the threshold but remained below the liberal 
threshold of 0.90, indicating adequate discriminant validity. 
 

4.2. Structural Model Assessment 
The structural model assessment examined path coefficients, significance levels, and explanatory power using 

bootstrapping procedures with 5,000 resamples. The results demonstrated significant relationships between key 
constructs and strong explanatory power for the dependent variables. 
 

Table 4. Direct Effects Results. 

Hypothesis Path β t-value p-value CI (95%) Decision 

H1 TR → BI 0.234 3.821 0.000 [0.123, 0.345] Supported 

H2 SC → BI 0.187 3.156 0.002 [0.089, 0.285] Supported 

H3 DQ → BI 0.219 3.743 0.000 [0.134, 0.304] Supported 

H4 MS → BI 0.298 4.967 0.000 [0.201, 0.395] Supported 

H5 OC → BI 0.176 2.891 0.004 [0.067, 0.285] Supported 

H6 AC → BI 0.267 4.321 0.000 [0.178, 0.356] Supported 

H7 CP → BI 0.143 2.543 0.011 [0.034, 0.252] Supported 

H8 CC → BI 0.156 2.789 0.005 [0.051, 0.261] Supported 

H9 BI → OE 0.678 12.543 0.000 [0.567, 0.789] Supported 

H10 BI → CRM 0.698 13.234 0.000 [0.589, 0.807] Supported 

H11 BI → IC 0.654 11.876 0.000 [0.543, 0.765] Supported 
Note: β = standardised path coefficient; CI = confidence interval. 

 
The direct effects analysis revealed significant positive relationships between all antecedent constructs and BI 

adoption intensity. Management support demonstrated the strongest effect (β = 0.298, p < 0.001), followed by 

analytical capabilities (β = 0.267, p < 0.001) and technological readiness (β = 0.234, p < 0.001). These findings 
support the theoretical proposition that organisational factors play particularly important roles in BI adoption 
decisions. 



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The relationships between BI adoption intensity and performance outcomes demonstrated strong positive 
effects across all three dependent variables. BI adoption showed the strongest effect on customer relationship 

management effectiveness (β = 0.698, p < 0.001), followed by operational efficiency (β = 0.678, p < 0.001) and 

innovation capabilities (β = 0.654, p < 0.001). These results support the theoretical proposition that BI adoption 
contributes to multiple dimensions of strategic performance. 
 

Table 5. Predictive Relevance Assessment. 

Construct R² Q² Effect Size (f²) 

BI Adoption Intensity 0.683 0.512 Large 
Operational Efficiency 0.459 0.324 Medium 
CRM Effectiveness 0.487 0.341 Medium 
Innovation Capabilities 0.428 0.296 Medium 

 
The predictive relevance assessment demonstrated strong explanatory power for the structural model. BI 

adoption intensity achieved an R² value of 0.683, indicating that the antecedent constructs explain 68.3% of the 
variance in BI adoption. The Q² values were all positive, indicating satisfactory predictive relevance for the model 
constructs. 
 

Table 6. Specific Indirect Effects. 

Indirect Path β t-value p-value CI (95%) 

TR → BI → OE 0.159 3.234 0.001 [0.078, 0.240] 

TR → BI → CRM 0.163 3.387 0.001 [0.082, 0.244] 

TR → BI → IC 0.153 3.156 0.002 [0.074, 0.232] 

MS → BI → OE 0.202 4.567 0.000 [0.123, 0.281] 

MS → BI → CRM 0.208 4.743 0.000 [0.129, 0.287] 

MS → BI → IC 0.195 4.432 0.000 [0.118, 0.272] 

AC → BI → OE 0.181 3.891 0.000 [0.103, 0.259] 

AC → BI → CRM 0.186 4.023 0.000 [0.108, 0.264] 

AC → BI → IC 0.175 3.743 0.000 [0.099, 0.251] 

 
The specific indirect effects analysis revealed significant mediation effects of BI adoption intensity on the 

relationships between antecedent constructs and performance outcomes. Management support demonstrated the 
strongest indirect effects across all performance dimensions, highlighting the critical role of executive commitment 
in translating BI investments into strategic benefits. 
 

4.3. Supplementary Analyses 
The supplementary analyses employed multigroup analysis (MGA) and fuzzy-set qualitative comparative 

analysis (fsQCA) to explore configurational patterns and contextual variations in BI adoption and performance 
outcomes. 
 

Table 7. Multigroup Analysis Results. 

Path Small Firms (n=156) Large Firms (n=156) p-value (MGA) 

TR → BI 0.298 0.187 0.032 

MS → BI 0.234 0.356 0.019 

AC → BI 0.312 0.223 0.041 

BI → OE 0.634 0.721 0.047 

BI → CRM 0.687 0.709 0.234 

BI → IC 0.623 0.685 0.189 
Note: MGA = Multigroup Analysis; p-values < 0.05 indicate significant group differences. 

 
The multigroup analysis revealed significant differences between small and large firms in several key 

relationships. Technological readiness showed stronger effects on BI adoption for small firms (β = 0.298) compared 

to large firms (β = 0.187), whilst management support demonstrated stronger effects for large firms (β = 0.356) 

compared to small firms (β = 0.234). These findings suggest that different factors drive BI adoption success across 
organisational contexts. 
 

Table 8. fsQCA Results - Configurations for High BI Adoption. 

Configuration TR SC DQ MS OC AC CP CC Consistency Coverage 

Config 1 ● ● ● ● ● ● ⊗ ⊗ 0.89 0.34 

Config 2 ● ⊗ ● ● ● ● ● ● 0.86 0.28 

Config 3 ● ● ⊗ ● ● ● ● ⊗ 0.84 0.26 

Config 4 ⊗ ● ● ● ● ● ● ● 0.82 0.23 
Note: ● = presence of condition; ⊗ = absence of condition; blank = don't care condition 

 
The fsQCA analysis identified four distinct configurational pathways to high BI adoption, each demonstrating 

consistency scores above 0.8 and meaningful coverage scores. Configuration 1 represents the "comprehensive 
readiness" pathway, characterised by strong technological, organisational, and data quality foundations but lower 
environmental pressures. Configuration 2 represents the "pressure-driven" pathway, emphasising environmental 
pressures and organisational capabilities whilst tolerating technological limitations. 
 
 
 



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Table 9. fsQCA Results - Configurations for High Performance. 

Configuration BI TR MS AC OC DQ Consistency Coverage 

High OE Config 1 ● ● ● ● ● ● 0.91 0.42 

High OE Config 2 ● ⊗ ● ● ● ● 0.87 0.31 

High CRM Config 1 ● ● ● ● ● ● 0.89 0.39 

High CRM Config 2 ● ● ● ● ⊗ ● 0.85 0.28 

High IC Config 1 ● ● ● ● ● ● 0.88 0.36 

High IC Config 2 ● ● ● ● ● ⊗ 0.84 0.27 

 
The fsQCA analysis for performance outcomes revealed that whilst BI adoption intensity is a necessary 

condition for high performance across all dimensions, different combinations of supporting factors contribute to 
optimal outcomes. High operational efficiency requires strong technological and organisational foundations, whilst 
high customer relationship management effectiveness can be achieved through alternative pathways emphasising 
either technological or cultural capabilities. 
 

5. Discussion of Research Results and Conclusions 
The empirical findings of this study provide compelling evidence for the complex, multifaceted nature of 

business intelligence adoption within Vietnam's retail sector, whilst demonstrating the significant strategic 
performance benefits that can be achieved through effective BI implementation. The results advance theoretical 
understanding by validating the integrated TOE-dynamic capabilities framework and revealing important 
configurational patterns that extend beyond traditional linear models. 

The significant positive relationships between all antecedent constructs and BI adoption intensity support the 
theoretical proposition that successful BI implementation requires favourable conditions across technological, 

organisational, and environmental dimensions. The particularly strong effect of management support (β = 0.298, p 
< 0.001) aligns with previous research emphasising the critical role of executive commitment in technology 

adoption processes (Popovič et al., 2012). This finding resonates with institutional theory's emphasis on the 
importance of organisational legitimacy and resource allocation in innovation adoption (DiMaggio & Powell, 

1983). The substantial effect of analytical capabilities (β = 0.267, p < 0.001) supports the dynamic capabilities 
perspective that technological resources must be complemented by human capabilities to achieve strategic benefits 
(Teece et al., 1997). 

The technological readiness construct demonstrated significant effects on BI adoption (β = 0.234, p < 0.001), 
supporting the TOE framework's emphasis on technological context factors. This finding aligns with previous 
research indicating that IT infrastructure maturity and technical expertise serve as foundational prerequisites for 
successful BI implementation (Wixom & Watson, 2001). However, the moderate effect size suggests that 
technological capabilities alone are insufficient for BI adoption success, supporting the study's integrated 
theoretical approach that emphasises the importance of organisational and environmental factors. 

The significant relationships between BI adoption intensity and all three performance dimensions provide 
strong empirical support for the theoretical proposition that BI technologies serve as enablers of strategic 

performance enhancement. The particularly strong effect on customer relationship management effectiveness (β = 
0.698, p < 0.001) supports previous research indicating that BI technologies provide substantial benefits for 
customer analytics and relationship management (Chen et al., 2012). The significant effects on operational 

efficiency (β = 0.678, p < 0.001) and innovation capabilities (β = 0.654, p < 0.001) demonstrate that BI adoption 
contributes to multiple dimensions of organisational performance, supporting the dynamic capabilities perspective 
that technological resources enhance sensing, seizing, and reconfiguring capabilities. 

The multigroup analysis results reveal important contextual variations in BI adoption patterns between small 

and large firms. The stronger effect of technological readiness for small firms (β = 0.298 vs. β = 0.187) suggests 
that resource constraints in smaller organisations make technological foundation particularly critical for BI 

adoption success. Conversely, the stronger effect of management support for large firms (β = 0.356 vs. β = 0.234) 
indicates that organisational complexity in larger entities requires stronger executive commitment to overcome 
implementation barriers. These findings support contingency theory perspectives that emphasise the importance of 
contextual factors in technology adoption processes (Lawrence & Lorsch, 1967). 

The fsQCA results provide particularly valuable insights by revealing multiple configurational pathways to 
successful BI adoption and performance outcomes. The identification of four distinct configurations for high BI 
adoption demonstrates the principle of equifinality, suggesting that organisations can achieve successful BI 
implementation through different combinations of technological, organisational, and environmental factors. This 
finding advances theoretical understanding by moving beyond simple linear models to recognise the complex, 
synergistic relationships between antecedent factors. 

The "comprehensive readiness" configuration emphasises the importance of strong technological and 
organisational foundations whilst tolerating lower environmental pressures. This pathway appears particularly 
relevant for organisations operating in stable competitive environments where internal capabilities drive BI 
adoption decisions. The "pressure-driven" configuration demonstrates that environmental pressures can 
compensate for technological limitations when combined with strong organisational capabilities, supporting 
institutional theory's emphasis on environmental influences on organisational behaviour (Scott, 2001). 

The fsQCA results for performance outcomes reveal that whilst BI adoption intensity serves as a necessary 
condition for high performance, different combinations of supporting factors contribute to optimal outcomes across 
performance dimensions. This finding supports the dynamic capabilities perspective that technological resources 
must be complemented by appropriate organisational capabilities to achieve strategic benefits. The identification of 
alternative pathways to high performance provides practical insights for organisations seeking to optimise their BI 
implementations. 

The study's theoretical contributions extend beyond empirical validation of existing frameworks to advance 
understanding of configurational complexity in technology adoption processes. The integration of TOE framework 



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with dynamic capabilities theory provides a more comprehensive theoretical model that can accommodate both 
contextual influences and capability development processes. This theoretical integration addresses limitations in 
existing literature that tends to focus on either environmental factors or organisational capabilities in isolation. 

The methodological contributions of this study demonstrate the value of combining traditional SEM 
approaches with configurational analysis methods. The fsQCA results provide insights that would not be apparent 
from SEM analysis alone, particularly regarding alternative pathways to successful outcomes. This methodological 
triangulation enhances the study's analytical depth and provides more comprehensive understanding of the 
complex phenomena under investigation. 

The practical implications of these findings are substantial for retail executives and policymakers in Vietnam 
and similar emerging market contexts. The identification of critical success factors and configurational pathways 
provides actionable insights for organisations planning BI implementations. The emphasis on management support 
and analytical capabilities highlights the importance of organisational readiness alongside technological 
investments. The configurational results suggest that organisations should assess their unique contexts to identify 
the most appropriate pathway for BI adoption success. 

The study's limitations include its cross-sectional design, which limits causal inference capabilities, and its 
focus on Vietnam's retail sector, which may limit generalisability to other contexts. Future research should employ 
longitudinal designs to examine the dynamic nature of BI adoption processes and extend the investigation to other 
industries and geographic contexts. Additionally, the study's emphasis on executive perspectives could be 
complemented by multi-level analyses that examine employee and customer perspectives on BI adoption outcomes. 

The findings contribute to the broader literature on digital transformation in emerging economies by 
demonstrating how organisations can successfully navigate technological adoption challenges whilst leveraging 
institutional and cultural factors to achieve strategic benefits. The study's emphasis on configurational complexity 
provides valuable insights for understanding how different combinations of factors contribute to successful digital 
transformation outcomes. These insights have broader implications for understanding technology adoption 
processes across emerging economies experiencing similar transitional dynamics. 

In conclusion, this study advances theoretical understanding of business intelligence adoption whilst providing 
practical insights for organisations seeking to achieve strategic performance benefits through BI implementation. 
The integration of multiple theoretical perspectives and methodological approaches demonstrates the value of 
comprehensive research designs for understanding complex organisational phenomena. The findings support the 
proposition that successful BI adoption requires synergistic combinations of technological, organisational, and 
environmental factors, whilst revealing multiple pathways to achieving superior performance outcomes. 
 

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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