The Commonplace
Home Dashboard Papers Evidence Digests 🎲
← Papers

AI innovation and 'recasting' are prerequisite for platform firms to gain competitive edge; three distinct AI-driven configurations—dominant, subsidiary, and collaborative—produce advantage, with innovation platforms outperforming transaction platforms.

How AI Enables Platform Enterprises to Build Competitive Advantages: A Configurational Analysis from the Perspective of Situated AI Theory
Xuguang Guo, Ying Teng, Huayong Du · March 25, 2026 · Systems
openalex correlational medium evidence 7/10 relevance DOI Source PDF
Using fsQCA on Chinese listed platform firms, the paper finds that AI technology innovation and recasting AI are necessary conditions and that three distinct configurations (situated-AI dominance, subsidiary, and collaborative drive) lead to platform competitive advantage, with innovation platforms more likely to succeed.

While existing research analyzes AI’s impact on platform enterprises’ competitive advantages from technological or organizational perspectives, it fails to adequately account for how multiple factors combined shape competitive advantages. From the perspective of situated AI theory, this study examines how the combinations among AI application characteristics, situated AI activities, platform enterprise attributes, and environmental characteristics collaboratively build platform enterprises’ competitive advantages. Drawing on panel data from Chinese listed platform enterprises and employing fuzzy-set Qualitative Comparative Analysis (fsQCA), this study reveals that (1) AI technology innovation and recasting AI are necessary conditions for platform enterprises to establish competitive advantages; (2) AI-enabled competitive advantages emerge from three types of configurations, the situated AI dominance type, the situated AI subsidiary type, or the collaborative drive type; (3) the AI-enabled combinations result in competitive advantages by three paths, AI internalization, AI leverage, and AI collaboration; and (4) the AI-enabled competitive advantages are more likely to be achieved by innovation platforms than by transaction platforms. These research findings fill the knowledge gap in AI-enabled competitive strategy, enrich the literature on situated AI theory, and offer practical guidance for platform enterprises’ AI applications.

Summary

Main Finding

AI-enabled competitive advantage for platform enterprises is not driven by single factors but by specific combinations of AI application characteristics, situated AI activities, firm attributes, and environmental factors. Using situated AI theory and fsQCA on panel data of Chinese listed platform firms, the study finds that AI technology innovation and “recasting AI” are necessary conditions, and that three distinct configurational types and three causal paths (internalization, leverage, collaboration) explain how platforms build competitive advantages. Innovation platforms are more likely than transaction platforms to realize these AI-enabled advantages.

Key Points

  • Necessary conditions:
    • AI technology innovation (ongoing AI R&D/technical capability).
    • Recasting AI (adapting/reconfiguring AI to fit situated contexts).
  • Three configurational types that lead to competitive advantage:
  • Situated AI dominance type — platform capability and situated AI activities are central drivers.
  • Situated AI subsidiary type — AI plays a supporting role alongside other firm or environmental strengths.
  • Collaborative drive type — competitive advantage emerges from collaboration/complementarity among multiple actors and resources.
  • Three causal paths (mechanisms) by which combinations yield advantage:
    • AI internalization — embedding AI capabilities inside the firm to transform products/processes.
    • AI leverage — using AI to amplify existing assets or market positions.
    • AI collaboration — jointly creating value with partners through AI-enabled complementarities.
  • Platform heterogeneity:
    • Innovation platforms (those focused on co-innovation, developer/ecosystem activities) are more likely than transaction platforms to achieve AI-enabled advantages.
  • Framing: Situated AI theory highlights that the effects of AI depend on context-specific activities and interactions among technological, organizational, and environmental factors.

Data & Methods

  • Data: Panel dataset of Chinese listed platform enterprises (sample period and sample size not specified in the summary).
  • Explanatory domains: AI application characteristics, situated AI activities, platform enterprise attributes, environmental characteristics.
  • Method: fuzzy-set Qualitative Comparative Analysis (fsQCA)
    • Identifies configurations (combinations of conditions) associated with the outcome (competitive advantage).
    • Distinguishes necessary conditions and multiple sufficient paths rather than estimating net effects of individual variables.
  • Outcomes: Identification of necessary conditions, three sufficient configuration types, and three pathways to AI-enabled competitive advantage.

Implications for AI Economics

  • Combinatorial causation: Economic impacts of AI on firm-level competitive advantage are configurational — complementarities and interactions matter more than isolated factors. Models and empirical work should account for nonlinearity and conjunctural effects.
  • Strategic heterogeneity: Different platform business models require distinct AI strategies (internalize vs. leverage vs. collaborate). One-size-fits-all policy or firm guidance toward AI adoption is unlikely to be optimal.
  • Role of innovation platforms: Because innovation platforms more readily realize AI-enabled advantages, economists and policymakers should treat platform type as a key moderator when assessing AI’s market effects, competition policy, and innovation dynamics.
  • Policy and investment signals:
    • Support for AI technology innovation and activities that enable recasting/adaptation of AI to local contexts can be high-leverage interventions.
    • Policies facilitating partnerships and ecosystem governance can enable the collaborative drive path.
  • Measurement and valuation: Investors and analysts should evaluate AI not only by R&D spending or single capabilities but by how AI is integrated, adapted, and combined with firm and environmental assets.
  • Research directions: Encourage studies that model complementarities, heterogeneity across platform types, and the institutional/environmental factors that enable different AI adoption paths.

Assessment

Paper Typecorrelational Evidence Strengthmedium — The study uses firm-level panel data and fsQCA to uncover consistent configurations associated with competitive advantage, which is well suited to identifying complex, conjunctural relationships; however, fsQCA does not provide counterfactual causal identification, is sensitive to calibration choices, and cannot rule out omitted-variable confounding or reverse causality as convincingly as experimental or strong quasi-experimental designs. Methods Rigormedium — Applying fsQCA to panel data and testing necessity/sufficiency demonstrates careful configurational analysis and aligns with the theoretical focus on combinations, but the approach depends on subjective calibration thresholds, limited transparency about robustness checks (e.g., threshold sensitivity, alternative codings), and lacks causal controls (fixed effects, instrumental variation) that would raise rigor to a high level. SamplePanel data of Chinese listed platform enterprises (firm-year units) including measures of AI application characteristics (e.g., AI technology innovation, 'recasting' AI), situated AI activities, platform attributes (innovation vs transaction platforms), environmental characteristics, and an outcome capturing firms' competitive advantage; exact sample years and number of firms/observations not specified in the summary. Themesinnovation org_design adoption IdentificationFuzzy-set Qualitative Comparative Analysis (fsQCA) on calibrated panel data: necessity and sufficiency analysis of conjunctural combinations of AI application characteristics, situated AI activities, platform attributes, and environmental characteristics; causal claims are inferred from set relations and configurations rather than from randomized or quasi-experimental variation or regression-based counterfactual identification. GeneralizabilityChina-only sample — regulatory, market, and institutional context may differ from other countries, Listed firms only — excludes SMEs and unlisted platforms, biasing toward larger, resource‑rich platforms, Platform-specific focus — results may not generalize to traditional non-platform firms, Findings depend on fsQCA calibration choices and variable operationalizations, which may limit replication in other datasets, Panel timeframe unspecified — results may be time-period dependent given rapid AI progress

Claims (5)

ClaimDirectionConfidenceOutcomeDetails
This study draws on panel data from Chinese listed platform enterprises and employs fuzzy-set Qualitative Comparative Analysis (fsQCA). Other positive high methodological approach / dataset used
0.3
AI technology innovation and recasting AI are necessary conditions for platform enterprises to establish competitive advantages. Firm Productivity positive high establish competitive advantages
0.3
AI-enabled competitive advantages emerge from three types of configurations: the situated AI dominance type, the situated AI subsidiary type, and the collaborative drive type. Firm Productivity positive high competitive advantages (presence via specific configurations)
0.3
The AI-enabled combinations produce competitive advantages through three paths: AI internalization, AI leverage, and AI collaboration. Firm Productivity positive high competitive advantages (mechanisms/paths)
0.3
AI-enabled competitive advantages are more likely to be achieved by innovation platforms than by transaction platforms. Adoption Rate positive high likelihood of achieving AI-enabled competitive advantages (innovation vs transaction platforms)
0.3

Notes