This study investigates how the use of artificial intelligence (AI) in brand name change decisions shapes

consumer responses. Drawing on perceived functional improvement, signaling theory, and resistance-to-change

frameworks, the research proposes and tests a structural model linking AI presence, perceived functional

improvement, brand trust, resistance to change, and purchase intention. Using PLS-SEM, findings show that AI

presence significantly enhances perceived functional improvement, which strengthens brand trust and

subsequently reduces consumer resistance. Lower resistance then increases purchase intention. The study

highlights the strategic value of AI as both a decision-support tool and a credibility signal in rebranding

Keywords: Artificial Intelligence; Brand Renaming; Perceived Functional Improvement; Brand Trust;

The integration of artificial intelligence (AI) into marketing practices is transforming not only operations

(automation, personalization) but also consumers' perceptions of brands. AI is used particularly in creative

processes such as generating, modifying, or replacing brand names. These strategic decisions, historically made

by human experts, are now increasingly being co-created or fully automated by AI systems capable of analyzing

linguistic structures, optimizing semantic consistency, and anticipating consumer preferences. Recent work has

shown that AI can improve the speed, creativity, and functional relevance of brand development, while reducing

However, replacing or changing a brand name is perceived as a risky change by consumers, likely to cause

confusion, disorientation, and resistance (Kapferer, 2007). It is a sensitive moment that can provoke rejection or

The objective of this study is to examine the effect of integrating artificial intelligence into the brand name

substitution process on improving functional perception, strengthening brand trust, and consequently reducing

This study raises major questions: To what extent does the presence of AI in the brand name substitution process

improve the perception of a functional improvement, strengthen brand trust, and help reduce consumer resistance

To answer these questions, a mixed-methods approach will be used, combining a literature review on the use of

AI in the brand name substitution process with an empirical study using a quantitative questionnaire.

Theoretically, this research contributes to the emerging literature on AI-assisted branding by demonstrating the

role of perceived functional improvement and trust in the acceptance of brand identity transformations. From a

name change decisions. Traditionally, renaming relied on managerial intuition and qualitative studies aimed at

anticipating consumer resistance (Kapferer, 2007). Several studies show that AI can rapidly generate name

proposals based on broad linguistic and semantic databases, while improving the relevance, consistency, and

creativity of the proposed options (Rege, 2025; Balabanova, 2025). Applied studies demonstrate that AI can

actively participate in the conceptual phases of rebranding; Hwang and Wu (2024) show that generative AI can

add value in rebranding workshops by proposing ideas that human designers can then use to ensure cultural

consistency. Applied studies demonstrate that AI can actively participate in the conceptual phases of rebranding.

Hwang and Wu (2024) show that generative AI can add value to rebranding workshops by suggesting ideas that

human designers can then use to ensure cultural consistency. At the strategic level, Gupta (2025) emphasizes that

AI can strengthen the clarity, consistency, and international adaptability of brand identities, particularly in the

signals a rigorous, neutral, and data-driven process, increasing the legitimacy of change (Spence, 1973).

Furthermore, uncertainty reduction theory indicates that AI helps mitigate perceived risks by simulating name

Finally, research on AI adoption in marketing shows that consumers attribute to AI the power of optimization

and neutrality, which reduces resistance when the algorithm is perceived as improving the quality of the decision

(Davenport, Guha, Grewal & Bressgott, 2020). Thus, AI is not merely a technical tool, but a cognitive and

AI can be seen as a tool for improving the relevance of a new name, which represents a functional improvement

for the consumer. According to Hartmann (2025), studies show that AI-generated visuals are sometimes judged

perceived usefulness is a major determinant of a technology's positive evaluation. Thus, the presence of AI in

Consumer trust rests on two pillars: the perception of competence and reliability (Mayer, Davis & Schoorman,

1995). AI, when it improves functional performance, acts precisely on the dimension of technical competence

Several studies show that the quality produced by AI increases trust in the technology (Hoff & Bashir, 2015; van

Pinxteren et al., 2019) but also in the company that integrates it. Thus, the perception of a functional improvement

mechanism can explain a decrease in resistance to change. According to Kapferer (2007) and Muzellec &

increased perceived risk, and a possible feeling of loss of brand identity. In the specific case of AI, when it

strengthened, thereby reducing cognitive and emotional resistance. According to Kim, Ferrin, and Rao (2008),

associated with change (Morgan & Hunt, 1994). In rebranding, high trust mitigates negative reactions (Muzellec

& Lambkin, 2006; Walsh et al., 2010). According to Longoni & Cian (2022), the use of AI perceived as beneficial

The theory of resistance to change (Oreg, 2003; Claudy et al., 2015) shows that resistance constitutes a cognitive,

emotional, and behavioral barrier that prevents the acceptance and adoption of new offerings. When this

resistance decreases, the consumer becomes more receptive and more willing to engage in behaviors favorable

to the product or brand. According to Kleijnen et al. (2009), a decrease in resistance increases openness to change

and predisposes consumers to adopt the new offering. In the context of technological innovation, a decrease in

To gather the information necessary to test the hypotheses mentioned above, a quantitative survey was conducted

online with 255 consumers aged 20 to over 50. A questionnaire was designed to collect data on consumer

perceptions of the variables selected based on the literature review. The questionnaire was pre-tested with 10%

of the sample to verify its clarity and precision and to eliminate any potential biases related to misunderstanding

the questions. This pre-test showed that all the questions were perfectly understandable to the respondents,

allowing us to continue data collection. Data collection took place from September 2nd to 30th, 2025. The details

To measure the variables in our study namely, AI presence, perceived functional improvement, trust, resistance,

Finally, resistance to change strongly and significantly influences purchase intention. This relationship shows

that the lower the resistance, the more substantially the purchase intention increases. This result aligns with the

literature on resistance to innovation (Walsh, G. et al. (2010); Claudy et al., 2015), which posits that removing

psychological barriers is a major determinant of the adoption and purchase of products associated with a brand

solely as a technical tool, but as a perceived actor influencing consumers' cognitive and behavioral judgments.

From a managerial perspective, the results indicate that companies should transparently highlight the role of AI

in creating or modifying their brand name to strengthen the credibility of the change, while combining human

expertise and AI to reassure consumers and reduce resistance. Furthermore, accompanying the rebranding with

clear communication about the functional benefits of the new name can foster buy-in and increase purchase

intent, confirming the importance of a change strategy focused on both perceived performance and trust.

However, several limitations must be highlighted: the sample used remains confined to a specific context, which

limits generalization; the measurement of AI presence relies on self-reported perceptions that may be influenced

by cognitive biases; and finally, the study focuses on a linear model that does not account for potential moderating

effects such as technological expertise, involvement in the product category, or sensitivity to innovation. Future