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

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

50204 - Business and management

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

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

ISBN

978-80-7678-372-0

ISSN

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e-ISSN

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Počet stran výsledku

16

Strana od-do

215-230

Název nakladatele

Academia Centrum UTB ve Zlíně

Místo vydání

Zlín

Místo konání akce

Zlín

Datum konání akce

9. 9. 2025

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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