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Feature-level AI tools—recommendations, chatbots and comparison engines—lift consumer trust and boost purchase intent for electronics, but privacy fears and cognitive costs blunt the gains; transparent, ethical design is therefore a commercial and regulatory priority.

Role of artificial intelligence on consumer buying behavior: the dual effects of AI-enabled features on decision-making and trust
Rishika Bhojwani, Justin Paul, Rajesh Srivastava · Fetched March 15, 2026 · Journal of Research in Interactive Marketing
semantic_scholar correlational low evidence 7/10 relevance DOI Source
Recommendation engines, chatbots, and comparison tools each increase consumer trust, perceived decision-making support, and purchase intention for electronic products, but privacy concerns, overreliance fears, and decision fatigue constrain adoption.

This study investigates the influence of Artificial Intelligence (AI) features on consumers’ buying behavior for electronic products, with a specific focus on consumer trust, decision-making ease, and purchase intention. This study used a quantitative research design to investigate the impact of key AI features on consumer outcomes. Data were collected using a structured questionnaire. Structural Equation Modeling (SEM) was employed to analyze the relationships between three AI features (modeled as latent constructs for recommendation engines, chatbots, and comparison tools) and the dependent variables of consumer trust, perceived decision-making support, and purchase intention. The results indicate that AI-enabled features significantly enhance consumer confidence and satisfaction by simplifying product evaluations and increasing perceived usefulness. However, concerns about privacy risks, overreliance on technology, and decision fatigue continue to shape consumer trust and adoption. This study highlights the importance of designing AI systems that are transparent, ethical, and inclusive for both tech-savvy and less technologically adept consumers. The originality of this study is threefold. First, it developed a unified framework that integrates technology acceptance and trust-based perspectives, a synthesis rarely found in the existing literature. Second, it moves beyond examining AI as a monolith by investigating how distinct and common AI features (recommendation engines, chatbots, and comparison tools) jointly influence the consumer decision journey. Finally, it bridges a critical theoretical gap by elucidating the interplay between perceived usefulness, trust, and ethical design, providing novel insights into how AI can be implemented not only effectively but also responsibly to empower consumers.

Summary

Main Finding

AI-enabled features (recommendation engines, chatbots, and comparison tools) significantly increase consumer trust, perceived decision-making support, and purchase intention for electronic products, but privacy concerns, overreliance, and decision fatigue moderate adoption—highlighting the need for transparent, ethical, and inclusive AI design.

Key Points

  • AI features were modeled separately (not as a single monolith): recommendation engines, chatbots, and comparison tools.
  • These features improve perceived usefulness and simplify product evaluations, raising consumer confidence and satisfaction.
  • Positive effects on purchase intention operate partly through increases in trust and perceived decision-making support.
  • Persistent negative influences include perceived privacy risks, fears of overreliance on technology, and potential for decision fatigue.
  • Original contributions:
    • Integrates technology acceptance and trust-based perspectives in a unified framework.
    • Disaggregates AI into distinct features to assess joint and feature-specific impacts on the consumer decision journey.
    • Clarifies the interplay between perceived usefulness, trust, and ethical design, emphasizing responsible implementation.

Data & Methods

  • Research design: quantitative, cross-sectional survey.
  • Data collection: structured questionnaire administered to consumers of electronic products (sample specifics not provided here).
  • Constructs:
    • Independent (latent): recommendation engines, chatbots, comparison tools.
    • Dependent: consumer trust, perceived decision-making support, purchase intention.
  • Analytical approach: Structural Equation Modeling (SEM) to estimate direct and mediated relationships among latent constructs.
  • Key outcomes: statistically significant positive paths from AI features to trust, decision support, and purchase intention; moderating/offsetting effects from privacy and cognitive costs.

Implications for AI Economics

  • Consumer surplus and adoption
    • AI features that increase perceived usefulness and trust can raise consumer adoption rates and willingness to pay, enhancing consumer surplus for digitally guided purchases.
    • Offsetting factors (privacy concerns, decision fatigue) limit full welfare gains; benefits depend on mitigating these risks.
  • Firm strategy and competition
    • Firms can differentiate via feature-level AI investments (recommendation quality, conversational agents, comparison tools), creating product-market segmentation and switching costs tied to perceived trust.
    • Firms investing in transparent, ethical AI may capture larger shares of trust-sensitive segments and command premium pricing.
  • Market efficiency and demand elasticity
    • Better decision-support tools reduce search frictions and information asymmetries, potentially increasing market efficiency and altering demand elasticities across product categories.
  • Platform dynamics and market power
    • High-performing AI features concentrated on major platforms can strengthen platform advantages and raise barriers to entry; transparency and interoperability become economically salient.
  • Regulation and data governance
    • Privacy risk as a constraint implies regulatory interventions (data protection, transparency mandates, explainability requirements) will materially affect adoption and design choices.
    • Economic trade-offs: stricter privacy regulation may reduce data-driven personalization benefits but increase trust, with ambiguous net welfare effects.
  • Distributional effects and inclusion
    • Heterogeneous impacts across tech-savvy and less adept consumers suggest potential distributional concerns—need for inclusive design to avoid widening digital divides in consumer welfare.
  • Research and policy priorities
    • Quantify welfare trade-offs between personalization gains and privacy costs; measure long-run effects on competition and market concentration.
    • Evaluate causal impacts (e.g., field experiments) to inform optimal regulatory and firm-level design choices that maximize consumer welfare while limiting harms.

Assessment

Paper Typecorrelational Evidence Strengthlow — Cross-sectional survey with SEM on self-reported measures; associations and mediated paths are identified statistically but there is no experimental or quasi-experimental variation to support causal claims—vulnerable to omitted variables, reverse causation, and common-method bias. Methods Rigormedium — Use of Structural Equation Modeling and disaggregation of AI features are appropriate and analytically rigorous for testing latent-variable relationships, but key design details (sampling frame, sample size, measurement validation, control variables, and handling of endogeneity) are not reported here; reliance on cross-sectional, self-reported data limits internal validity. SampleStructured cross-sectional questionnaire administered to consumers of electronic products; latent constructs measured for three AI features (recommendation engines, chatbots, comparison tools), consumer trust, perceived decision-making support, and purchase intention; specific sample size, sampling frame, recruitment method, demographics, and geographic scope not provided in the summary. Themesadoption human_ai_collab GeneralizabilitySample composition and recruitment method not reported (unknown representativeness), Findings limited to consumers of electronic products and may not generalize to other product categories, Cross-sectional, self-reported measures subject to response and common-method bias, Cultural and regulatory context not specified (limits cross-country generalization), Likely overrepresentation of tech-savvy respondents if administered online, Effects observed on intentions rather than revealed purchasing behavior

Claims (11)

ClaimDirectionConfidenceOutcomeDetails
AI-enabled features significantly enhance consumer confidence and satisfaction by simplifying product evaluations and increasing perceived usefulness. Consumer Welfare positive medium consumer confidence / satisfaction (linked to perceived usefulness and ease of product evaluation)
0.09
Distinct AI features (recommendation engines, chatbots, and comparison tools) influence consumer outcomes when modeled as latent constructs. Consumer Welfare mixed high influence on consumer trust, perceived decision-making support, and purchase intention
0.15
AI features positively affect consumer trust. Ai Safety And Ethics positive medium consumer trust
0.09
AI features improve perceived decision-making support (i.e., ease of decision-making / simplification of product evaluation). Decision Quality positive medium perceived decision-making support (ease of decision-making / simplification of product evaluation)
0.09
AI features increase consumers' purchase intention for electronic products. Consumer Welfare positive medium purchase intention
0.09
Concerns about privacy risks, overreliance on technology, and decision fatigue continue to shape consumer trust and adoption of AI features. Ai Safety And Ethics negative medium consumer trust and adoption (barriers: privacy concerns, overreliance, decision fatigue)
0.09
Designing AI systems that are transparent, ethical, and inclusive is important to support adoption among both tech-savvy and less technologically adept consumers. Adoption Rate positive low adoption and trust across consumer segments (tech-savvy vs. less technologically adept)
0.04
This study developed a unified framework that integrates technology acceptance and trust-based perspectives. Research Productivity null_result medium theoretical integration (no direct empirical outcome variable; framework development)
0.09
The study moves beyond treating AI as a monolith by empirically investigating how distinct AI features jointly influence the consumer decision journey. Research Productivity positive high joint influence on consumer decision journey outcomes (trust, decision support, purchase intention)
0.15
The study clarifies the interplay between perceived usefulness, trust, and ethical design, offering insights into responsible AI implementation to empower consumers. Ai Safety And Ethics mixed medium perceived usefulness, consumer trust, and implications for ethical design (theoretical and practical implications rather than a single measured outcome)
0.09
Data were collected using a structured questionnaire and analyzed using Structural Equation Modeling (SEM). Other null_result high methodology (data collection and analysis technique)
0.15

Notes