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Cited by 40AI in Service InteractionsConsumer Behavior and Marketing InfluenceTechnology Adoption and User Behaviour

Exploring the role of AI algorithmic agents: The impact of algorithmic decision autonomy on consumer purchase decisions

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Fan, Yuejiao, Liu, Xianggang·2022·Frontiers in Psychology
DOI 10.3389/fpsyg.2022.1009173PMC PMC9630471
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Abstract

Although related studies have examined the impact of different images of artificial intelligence products on consumer evaluation, exploring the impact on consumer purchase decisions from the perspective of algorithmic decision autonomy remains under-explored. Based on the self-determination theory, this research discusses the influence of the agent decision-making role played by different AI algorithmic decision autonomy on consumer purchase decisions. The results of the 3 studies indicate that algorithmic decision autonomy has an inverted U-shaped effect on consumer’s purchase decisions, consumer’s self-efficacy mediates the relationship between algorithmic decision autonomy and purchase decisions, and consumer’s power distance moderates the relationship between algorithmic decision autonomy, self-efficacy, and purchase decisions. The research results can provide references for marketers, retailers, algorithm designers, and other parties to formulate algorithm marketing strategies, make AI algorithm decisions better serve consumers, and achieve value co-creation with consumers.

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  • Autonomy should be seen as a boundary condition that affects the delegation mechanism rather than as a simple main effect.

    “This suggests that autonomy should be seen as a boundary condition that affects the delegation mechanism rather than as a simple main effect [40,46].”

    From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [46]

    In “Overview of studies > Study 1: Main effect” (page unavailable), the paper reports an inverted U-shaped main effect of algorithmic decision autonomy on purchase decisions. Its Theoretical implications section (page unavailable) identifies power distance—not autonomy—as the moderating boundary condition. This is contrary to the claim that autonomy should be treated as a boundary condition rather than a main effect. (LLM, conf=0.88)

    Checked by a reasoning model — may be wrong.

    Experimental — accuracy not yet measured

  • The net-positive pattern is evident in dominant satisfaction/continuance pathways according to numerous studies.

    “According to numerous studies [44,46,48,50,69], this pattern is evident in dominant satisfaction/continuance pathways.”

    From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [46]

    The theoretical-background section on algorithmic decision autonomy and purchase decisions says that appropriately supportive algorithmic decisions can increase users’ satisfaction and loyalty. It does not establish dominant satisfaction/continuance pathways; the reported studies instead examine purchase decisions and self-efficacy, including an inverted-U relationship. No page number is available in our copy. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • In the main empirical cluster (Autonomy-1, recommendation/support scope), positive outcomes usually follow a utility/assurance chain: performance cues and interaction quality make people feel more useful and satisfied, which builds trust and leads to downstream adoption/continuance or purchase-intent outcomes.

    “In the main empirical cluster (Autonomy-1, recommendation/support scope), positive outcomes usually follow a utility/assurance chain: performance cues and interaction quality make people feel more useful and satisfied, which builds trust and leads to downstream adoption/continuance or purchase-intent outcomes (e.g., [44,46,48,50,69]).”

    From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [46]

    Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • Experimental research frequently substantiates causal assertions when randomization is evident and manipulation checks are documented, but limitations endure, including scenario-based autonomy, restricted domains, and intention-laden outcomes.

    “Table 11 demonstrates that experimental research frequently substantiates causal assertions when randomization is evident and manipulation checks are documented; nonetheless, limitations endure, characterized by scenario-based autonomy, restricted domains, and intention-laden outcomes [40,41,46].”

    From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [46]

    Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • The autonomy-to-powerlessness mechanism is well-supported in controlled experiments but often not tested in real-life transactions.

    “The autonomy-to-powerlessness mechanism is well-supported in controlled experiments, but it is often not tested in real-life transactions [40,46].”

    From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [46]

    Study 3 (Overview of studies; no page given) reports that high algorithmic decision autonomy reduced self-determination and self-efficacy among participants with high power distance. The study used controlled scenarios, including a simulated e-commerce shopping guide, rather than observed transactions. But the paper does not establish that this mechanism is *often* untested in real-life transactions, and it measures self-efficacy rather than powerlessness directly. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Authors
Fan, Y., & Liu, X.
Affiliations
Huaqiao University (CN)
Corresponding author
Liu, Xianggang
ORCID
Liu, Xianggang
Venue
Frontiers in Psychology
Publisher
Frontiers Media SA
Published
2022-10-20
Volume
13
Pages
1009173-1009173
ISSN
1664-1078 (ISSN-L)
DOI
10.3389/fpsyg.2022.1009173
PMC
PMC9630471
OpenAlex
W4306947088
Cited by
40
References
94
Citations by year
2026: 19 · 2025: 16 · 2024: 4 · 2023: 1
FWCI
1.73
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Psychology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Social Science Fund of China
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
  • Social Sciences › Business, Management and Accounting › Marketing › Consumer Behavior and Marketing Influence
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
Keywords
purchase decision, self-determination theory, self-efficacy, power distance, inverted U-shaped relationship, value co-creation, consumer trust in AI

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