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Southeast Asian firms that pair AI and Big Data with a sustainability focus report sharper marketing agility and higher innovation, which together explain most of the variation in competitive advantage; however, findings rest on managers' cross‑sectional perceptions rather than causal evidence.

Strategic Marketing Transformation in the Digital Age: Integrating AI, Big Data, and Sustainability for Competitive Advantage in Southeast Asia
Anthonius S Hutabarat, Irawan R D Budianto · January 20, 2026 · Journal of Accounting and Finance Management
openalex correlational low evidence 7/10 relevance Summary only summary available; pdf_status=paywall DOI Source PDF

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Survey evidence from 412 senior marketing executives in five Southeast Asian countries shows that AI adoption, Big Data analytics capability, and sustainability orientation are positively associated with marketing agility and innovation performance, which in turn strongly predict perceived competitive advantage.

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In the dynamic business environment of Indonesia and Southeast Asia (SEA), organizations must navigate rapid technological advancements, shifting consumer expectations, and intensifying global competition. The convergence of Artificial Intelligence (AI), Big Data analytics, and sustainability orientation offers unprecedented opportunities for strategic marketing transformation, yet empirical research integrating these dimensions remains limited in emerging market contexts. Drawing on Dynamic Capabilities Theory (Teece, 2016), the Technology Acceptance Model (Davis, 1989), and the Sustainable Marketing Framework (Belz & Peattie, 2012), this study develops and empirically tests a comprehensive model linking AI adoption, Big Data analytics capability, and sustainability orientation to marketing agility, innovation performance, and competitive advantage. Data were collected from 412 senior marketing executives in Indonesia, Malaysia, Singapore, Thailand, and Vietnam, representing the FMCG, retail, technology, hospitality, and manufacturing sectors. Using Structural Equation Modeling–Partial Least Squares (SEM-PLS), results indicate that AI adoption, Big Data analytics capability, and sustainability orientation each significantly enhance marketing agility and innovation performance, which in turn strongly predict competitive advantage (R² = 0.68). All seven hypotheses were supported, confirming the robustness of the model. The findings contribute theoretically by integrating technological and sustainability perspectives within a strategic marketing framework for emerging markets, and offer actionable insights for managers in SEA seeking to leverage digital transformation for sustained market leadership.

Summary

Main Finding

AI adoption, Big Data analytics capability, and sustainability orientation each significantly improve marketing agility and innovation performance in firms across Southeast Asia; those two capabilities together strongly predict competitive advantage (model R² = 0.68). All seven hypothesized paths in the proposed model were supported, demonstrating a robust link between technological/sustainability investments and strategic market outcomes in emerging-market firms.

Key Points

  • Theoretical integration: the study combines Dynamic Capabilities Theory, the Technology Acceptance Model, and the Sustainable Marketing Framework to explain how technology and sustainability jointly drive marketing outcomes.
  • Core relationships tested and supported:
    • AI adoption → marketing agility
    • AI adoption → innovation performance
    • Big Data analytics capability → marketing agility
    • Big Data analytics capability → innovation performance
    • Sustainability orientation → marketing agility
    • Sustainability orientation → innovation performance
    • Marketing agility and innovation performance → competitive advantage
  • Effectiveness: the model explains 68% of variance in competitive advantage (R² = 0.68), indicating strong predictive power.
  • Context and scope: results are drawn from multiple industries (FMCG, retail, technology, hospitality, manufacturing) across five SEA countries (Indonesia, Malaysia, Singapore, Thailand, Vietnam).

Data & Methods

  • Sample: 412 senior marketing executives in Indonesia, Malaysia, Singapore, Thailand, and Vietnam.
  • Sectors represented: fast-moving consumer goods (FMCG), retail, technology, hospitality, manufacturing.
  • Measurement: survey-based constructs for AI adoption, Big Data analytics capability, sustainability orientation, marketing agility, innovation performance, and competitive advantage.
  • Analytical approach: Structural Equation Modeling using Partial Least Squares (SEM-PLS), appropriate for complex predictive models and moderate sample sizes.
  • Findings: All seven hypothesized causal paths were statistically significant; the mediation pathway where marketing agility and innovation performance transmit the effects of AI/Big Data/sustainability onto competitive advantage is supported by the pattern of results.

Implications for AI Economics

  • Firm-level productivity and returns to AI:
    • Evidence that AI adoption and Big Data capabilities translate into operational and strategic gains (marketing agility, innovation), supporting the view that AI investments can generate measurable firm-level productivity improvements in emerging markets.
    • High R² for competitive advantage suggests meaningful private returns to these investments, strengthening the case for firm-level capital allocation to AI and data infrastructure.
  • Complementarities and bundling:
    • Complementary assets matter: sustainability orientation acts alongside AI and analytics to enhance outcomes, implying that returns to AI are conditional on organizational practices and non-technological capabilities—important when modeling returns to digital capital.
  • Market structure and competition:
    • If early adopters realize substantial competitive advantage, adoption may contribute to winner-takes-most dynamics in some sectors, with implications for market concentration, entry barriers, and platform effects in SEA markets.
  • Labor, skills, and distributional effects:
    • Gains from AI/data appear mediated by marketing agility and innovation—activities that rely on managerial and analytical skills—implying rising demand (and wage premia) for data-literate marketers and AI-savvy managers; policy/intervention around reskilling is economically relevant.
  • Policy and infrastructure:
    • To realize broader economic gains, public policy should lower frictions to data access, invest in digital infrastructure, and support data governance frameworks that preserve competitive markets while enabling firm-level analytics.
  • Measurement and empirical research directions:
    • Economic models and empirical work should incorporate complementarities between AI, data capabilities, and organizational factors (e.g., sustainability orientation) when estimating returns to AI.
    • Future empirical work should use longitudinal or administrative performance data to estimate causal effects, quantify social vs. private returns, and explore heterogeneity across sectors and firm sizes.
  • Risk and regulation considerations:
    • Competitive advantages from AI may incentivize aggressive data strategies; regulators should monitor anticompetitive behavior and data monopolization risks while balancing innovation incentives.

Overall, the study supplies empirical support that AI and Big Data, when combined with sustainability-oriented organizational practices, are productive investments that enhance firms' strategic capabilities and competitive positions in emerging Southeast Asian markets—insights that should inform economic models of technology diffusion, firm heterogeneity, and policy design.

Assessment

Paper Typecorrelational Evidence Strengthlow — Cross-sectional, self-reported survey data with SEM-PLS provides correlational associations but no credible causal identification; results are vulnerable to reverse causality, omitted variables, and common-method bias despite a reasonable sample size. Methods Rigormedium — Appropriate and common analytical tool (SEM-PLS) for testing multi-construct models and a fairly large, multi-country sample (n=412) increase internal consistency and statistical power, but rigor is limited by non-probability sampling, cross-sectional design, reliance on self-report measures, and lack of quasi-experimental controls or instrumental variation. SampleCross-sectional survey of 412 senior marketing executives from Indonesia, Malaysia, Singapore, Thailand, and Vietnam, covering FMCG, retail, technology, hospitality, and manufacturing sectors (sampling approach not specified, likely purposive/convenience; firm size and other firm-level characteristics not reported in excerpt). Themesinnovation adoption org_design human_ai_collab GeneralizabilityLimited to senior marketing executives (managerial perceptions rather than objective firm outcomes), Geographically restricted to five Southeast Asian countries (emerging-market context), Sector coverage concentrated in selected industries (FMCG, retail, tech, hospitality, manufacturing) and may not generalize to others, Cross-sectional, self-reported measures limit inference to other time periods or objective performance metrics, Likely non-probability sampling reduces representativeness, Cultural, regulatory, and market-structure heterogeneity across SEA may affect external validity

Claims (12)

ClaimDirectionOutcomeConfidence & EvidenceDetails
Data were collected from 412 senior marketing executives in Indonesia, Malaysia, Singapore, Thailand, and Vietnam, representing the FMCG, retail, technology, hospitality, and manufacturing sectors. Other null_result sample_composition
Reading fidelity high
Study strength high
n=412
0.5
The study used Structural Equation Modeling–Partial Least Squares (SEM-PLS) to analyze the data. Other null_result analysis_method
Reading fidelity high
Study strength high
n=412
0.5
AI adoption significantly enhances marketing agility. Organizational Efficiency positive marketing agility
Reading fidelity high
Study strength medium
n=412
0.3
AI adoption significantly enhances innovation performance. Innovation Output positive innovation performance
Reading fidelity high
Study strength medium
n=412
0.3
Big Data analytics capability significantly enhances marketing agility. Organizational Efficiency positive marketing agility
Reading fidelity high
Study strength medium
n=412
0.3
Big Data analytics capability significantly enhances innovation performance. Innovation Output positive innovation performance
Reading fidelity high
Study strength medium
n=412
0.3
Sustainability orientation significantly enhances marketing agility. Organizational Efficiency positive marketing agility
Reading fidelity high
Study strength medium
n=412
0.3
Sustainability orientation significantly enhances innovation performance. Innovation Output positive innovation performance
Reading fidelity high
Study strength medium
n=412
0.3
Marketing agility and innovation performance strongly predict competitive advantage (R² = 0.68). Firm Productivity positive competitive advantage
Reading fidelity high
Study strength medium
n=412
R² = 0.68
0.3
All seven hypotheses were supported, confirming the robustness of the model. Other positive hypothesis_support
Reading fidelity high
Study strength medium
n=412
0.3
The study contributes theoretically by integrating technological (AI, Big Data) and sustainability perspectives within a strategic marketing framework for emerging markets. Other positive theoretical_integration
Reading fidelity high
Study strength speculative
n=412
0.05
The findings offer actionable insights for managers in Southeast Asia seeking to leverage digital transformation for sustained market leadership. Other positive managerial_insights
Reading fidelity high
Study strength speculative
n=412
0.05

Notes