Cited by 52AI in Service InteractionsDigital Marketing and Social MediaConsumer Behavior in Brand Consumption and Identification

Anthropomorphism in artificial intelligence: a game-changer for brand marketing

Open access
Gomes, Sofia, Lopes, João M., Nogueira, Elisabete·2025·Future Business Journal

Abstract

The expansion of brands adopting artificial intelligence services with human characteristics, such as chatbots, has revolutionized digital marketing strategies, transforming how companies reach and engage customers. This technological innovation is reshaping digital interactions between companies and consumers, offering a more personalized and efficient experience. This study explores the influence of chatbot anthropomorphism on customer engagement and purchasing decision-making with brands that use this artificial intelligence service. Data from a questionnaire with 1319 participants was analyzed using partial least squares method. Chatbot anthropomorphism has a more positive influence on purchasing decision-making when this relationship is mediated by customer engagement. In turn, customer involvement also has a positive influence on decision-making. The results emphasize that for a greater influence of the chatbot's anthropomorphism on purchasing decision-making, the customer must first be engaged with the chatbot. This research illuminates a new path in the domain of AI-enabled brand interactions, showing the distinct influence of anthropomorphism in chatbots on customer satisfaction, trust and loyalty, thus revolutionizing traditional paradigms of consumer-brand engagement and decision-making processes. By exploring the intricate dynamics between customer engagement with anthropomorphized chatbots and purchasing decisions, this study breaks new ground, offering unprecedented insight into the transformative potential of human-like chatbot interactions in shaping consumer behavior and brand relationships.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s43093-025-00423-y

Cites

73 of 73 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Consciousness attributions to chatbots based on biased belief are likely to increase, as AI companies have incentives to facilitate anthropomorphically biased conceptions.

    “consciousness attributions to them based on biased belief are likely to increase, as AI companies have incentives to facilitate anthropomorphically biased conceptions (e.g., to increase user engagement) (Gomes et al., 2025).”

    From Are Attributions of Consciousness to AI Chatbots Epistemically Innocent? (2026) · cites this paper as Gomes et al., 2025

    The Introduction and Discussion say brands can benefit from anthropomorphic chatbot design because it increases customer engagement, supporting the claim about commercial incentives. The section on chatbot anthropomorphism also warns that human-like design can create unrealistic expectations. The paper does not say that biased attributions of consciousness are likely to increase. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 1

  1. Gartner (2023) Gartner survey reveals only 8% of customers used a chatbot during their most recent customer service interaction. In: Gartner. https:// www. gartn er. com/ en/ newsr oom/ press-relea ses/ 2023- 06-15-gartn er-survey-revea ls-only-8-perce nt-of-custo mers-used-a-chatb ot-during-their-most-recent-custo mer-servi ce-inter action. Accessed 17 April 2024

Cited by

Sorted by citations

Authors
Gomes, S., Lopes, J. M., & Nogueira, E.
Affiliations
  • Universidade Portucalense (PT)
  • University of Beira Interior (PT)
Corresponding author
Lopes, João M.
Venue
Future Business Journal
Published
2025-01-15
Volume
11
Issue
1
ISSN
  • 2314-7202 (ISSN-L)
  • 2314-7210
OpenAlex
W4406358028
Cited by
52
References
73
Citations by year
2026: 33 · 2025: 18 · 2024: 1
FWCI
20.46
Type
journal article
Language
en
Open access
Locations
  • Future Business Journal (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
  • Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, cc-by-nc-nd)
Funders
  • NECE - Research Center in Business Sciences, University of Beira Interior (UIDB/04630/2020)
  • Fundação para a Ciência e a Tecnologia (UIDB/04630/2020)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
  • Social Sciences › Sociology and Political Science › Digital Marketing and Social Media
  • Social Sciences › Business, Management and Accounting › Marketing › Consumer Behavior in Brand Consumption and Identification
Keywords
chatbots, customer engagement, purchase decision, customer involvement, brand marketing, digital marketing, consumer-brand interaction, customer satisfaction, consumer trust, customer loyalty

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review