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CONSUMER TRUST IN ARTIFICIAL INTELLIGENCE: A SYSTEMATIC LITERATURE REVIEW USING PRISMA GUIDELINES

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28120%2F25%3A63593158" target="_blank" >RIV/70883521:28120/25:63593158 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    CONSUMER TRUST IN ARTIFICIAL INTELLIGENCE: A SYSTEMATIC LITERATURE REVIEW USING PRISMA GUIDELINES

  • Original language description

    This systematic literature review examines how consumer trust in artificial intelligence (AI) is conceptualized, measured and influenced in marketing contexts. Using PRISMA 2020 guidelines, 64 empirical studies published between 2017 and 2024 in ABS-ranked journals were analyzed. The findings highlight that trust is a key driver of AI adoption, satisfaction and engagement, especially in e-commerce, banking and social media marketing. Most studies define trust through cognitive dimensions such as competence and reliability, while emotional or relational aspects are less frequently addressed. Trust is commonly measured using self-reported Likert scales, with little use of behavioral or longitudinal data. Influencing factors include AI design attributes, user characteristics and contextual elements like brand reputation. The review identifies a lack of unified theoretical models and limited research on affective trust, generative AI and underrepresented user groups, offering clear directions for future research.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    50204 - Business and management

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2025

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Conference Proceedings DOKBAT 2025 21st International Bata Conference for Ph.D. Students and Young Researchers

  • ISBN

    978-80-7678-372-0

  • ISSN

  • e-ISSN

  • Number of pages

    16

  • Pages from-to

    215-230

  • Publisher name

    Academia Centrum UTB ve Zlíně

  • Place of publication

    Zlín

  • Event location

    Zlín

  • Event date

    Sep 9, 2025

  • Type of event by nationality

    EUR - Evropská akce

  • UT code for WoS article