SMEs that pair short-video marketing with AI content tools report stronger digital trust, and that trust is linked to higher self-reported performance on TikTok and Shopee; however, the evidence is associative from a cross-sectional survey.

Main Finding

Digital trust is the central mechanism linking content strategy and AI adoption to SME performance on social commerce platforms: both short-video marketing and AI-assisted content tools significantly increase digital trust, and that trust in turn has a strong positive effect on SME business performance. Digital trust significantly mediates the effects of both short-video marketing and AI content tools on performance.

Key Points

• Novelty: Integrates short-video marketing and AI-assisted content creation into a single trust-based explanatory model for SMEs on social commerce (TikTok and Shopee) in a developing-country context.
• Main relationships tested:
  • Short-video marketing → Digital trust (positive, significant)
  • AI content tools → Digital trust (positive, significant)
  • Digital trust → SME business performance (strong positive, significant)
  • Digital trust mediates short-video marketing → business performance and AI content tools → business performance (both mediation effects significant)
• Practical takeaway: Investing in short-video strategies and AI content tools helps build consumers’ digital trust, which is the key channel through which such investments translate into improved business outcomes.

Data & Methods

• Design: Quantitative explanatory study using cross-sectional survey data.
• Sample: 205 SMEs actively operating on TikTok and Shopee.
• Measures: Core constructs were short-video marketing, AI-assisted content tools, digital trust, and SME business performance (self-reported).
• Analysis: Partial Least Squares Structural Equation Modeling (PLS-SEM) implemented in SmartPLS to estimate direct and mediating effects.
• Limitations to note: cross-sectional self-reported data (risk of common-method bias), modest sample size, and context restricted to a developing-country social-commerce environment—limits causal claims and generalizability.

Implications for AI Economics

• Adoption incentives and ROI: The results imply that AI content tools deliver economic value to SMEs primarily via trust-building, not just task automation or cost savings. Evaluations of AI investments should incorporate trust-related returns alongside productivity gains.
• Market competitiveness and diffusion: By increasing trust (and thereby performance), AI-assisted content tools can lower barriers for SMEs to compete on platforms that emphasize short-form video—potentially accelerating diffusion of such tools among small sellers.
• Platform design and policy: Platforms (e.g., TikTok, Shopee) should consider features and governance that reinforce digital trust (transparent AI-tool disclosures, content authenticity signals, seller verification), since trust amplifies the economic value of content and AI tools.
• Labor and complementarities: AI content tools complement creative marketing (short-video strategies) rather than simply replacing labor; policy and firm-level decisions should account for complementarities and skill upgrading needs, not only displacement.
• Externalities and market structure: Widespread adoption of AI content tools may shift attention dynamics and competition on platforms—platforms and regulators should monitor potential concentration effects (e.g., winners who best leverage trust-building content).
• Measurement and future empirical work: AI economics research should move beyond self-reports to longitudinal, experimental, or platform-record analyses to identify causal channels, quantify effect sizes (e.g., sales lift per unit increase in trust), and assess welfare implications. Additional variables to integrate (as the study recommends) include platform reputation, influencer endorsement, consumer digital literacy, data privacy perceptions, and recommendation algorithm dynamics.

Suggestions for next steps in research and practice: - Use longitudinal or quasi-experimental designs to establish causal effects and persistence of trust-related gains. - Collect objective performance metrics (sales, conversion rates) from platforms to validate self-reported outcomes. - Model interactions among AI tools, influencer activity, and platform recommender systems to understand complementarities and strategic effects. - Evaluate distributional outcomes (which SMEs benefit most) and the net welfare impacts of AI-enabled trust-building on consumers and sellers.