Shoppers who perceive AI-powered personalization and smoother operations report higher satisfaction and greater intent to keep using e-commerce platforms. The finding is based on cross-sectional survey regressions from Bosnia and Herzegovina and indicates association rather than proven causation.

Main Finding

AI-driven functionalities in e-commerce — especially perceived personalization, convenience, and operational efficiency — significantly increase customer satisfaction, which in turn increases continuance intention to use platforms. Privacy concerns and perceived security showed only limited statistical impact in the study. Overall, AI contributes to e‑commerce development primarily via performance-based improvements in the user experience.

Key Points

• Conceptual model: perceived AI-enabled functionalities (personalization, convenience, operational efficiency, security, privacy concerns) → customer satisfaction → continuance intention.
• Main hypotheses (H1–H6):
  • H1 (personalization → satisfaction), H2 (convenience → satisfaction), H3 (operational efficiency → satisfaction) — supported.
  • H4 (security → satisfaction) — limited/weak effect.
  • H5 (privacy concerns → satisfaction) — negative but limited effect.
  • H6 (satisfaction → continuance intention) — supported.
• AI applications discussed: recommender systems, chatbots/virtual assistants, intelligent visual and voice search, virtual personal shoppers, fake-review detection, assortment/intelligence tools.
• Benefits emphasized: increased relevance and personalization, faster and more convenient interactions, operational efficiencies (inventory, pricing, logistics) and improved after-sales support.
• Challenges highlighted: data privacy and security risks, high implementation costs, need for skilled personnel, potential algorithmic bias, regulatory and ethical issues.
• Literature basis: broad review of 113 academic sources (1942–2024), combining historical foundations and recent empirical work.

Data & Methods

• Empirical setting: survey data collected in Bosnia and Herzegovina (consumer-perspective focus).
• Analyses conducted: reliability analysis, descriptive statistics, correlation analysis, and multiple regression models.
  • Regression Model 1: determinants of customer satisfaction (predictors: perceived personalization, convenience, operational efficiency, security, privacy concerns).
  • Regression Model 2: impact of customer satisfaction on continuance intention.
• Findings derive from regression coefficients and significance tests showing strong positive effects for personalization, convenience, operational efficiency on satisfaction, and satisfaction on continuance intention; privacy/security had limited statistical impact.
• Methodological limitations noted by author: cross-sectional survey, consumer-focused (does not directly measure firm-level performance or long-term outcomes), geographically limited sample (Bosnia and Herzegovina), and potential generalizability constraints.

Implications for AI Economics

• Value drivers and ROI:
  • Firms gain measurable consumer-retention value by investing in personalization, convenience-enhancing features, and operational-efficiency AI tools. These are likely to yield higher customer satisfaction and increased continuance, improving lifetime customer value.
  • Because privacy/security concerns had limited measured impact here, short-term firm ROI calculations may prioritize performance features — but this must be weighed against regulatory risk and potential latent trust losses.
• Market structure and competition:
  • AI-enabled personalization and recommendation systems strengthen platform differentiation and switching costs, potentially increasing market concentration in platforms that scale data and models effectively.
  • Operational efficiencies (inventory/pricing optimization) can reduce marginal costs and enable more aggressive competitive strategies (dynamic pricing, faster fulfillment).
• Labor and productivity:
  • Adoption of chatbots and automation substitutes routine customer-service tasks; economic effects include reallocation of labor toward higher-skill roles and potential short-term displacement in routine jobs.
• Data governance and regulation:
  • Even if privacy concerns appeared limited in this sample, policy risk remains important. Economists and firms should account for regulatory uncertainty (data-protection laws, liability rules) when modeling investment and expected returns.
  • Effective data governance frameworks and transparent AI practices can mitigate regulatory and reputational risks, preserving long-run demand.
• Research and measurement suggestions for economists:
  • Extend analysis to firm-level outcomes (sales, conversion rates, churn, margins) to quantify direct economic returns of AI features.
  • Use panel or experimental designs to identify causal effects and long-term dynamics (learning, habituation, privacy backlash).
  • Study distributional effects across firm sizes and market segments — smaller firms may face higher fixed costs, altering competitive dynamics.
• Policy implications:
  • Regulators should balance fostering innovation (productivity gains, better consumer matches) with safeguards on privacy, bias, and consumer protection to avoid negative externalities.
  • Support for skills and retraining programs can ameliorate labor-market frictions from AI adoption in e-commerce.

If you want, I can (a) extract or estimate specific regression coefficients and sample details if you provide the empirical tables/appendices; (b) produce a short slide-style summary for presentations; or (c) draft policy recommendations tailored to regulators or e‑commerce firms.