Ethical Advertising Disclosure, AI-Content Transparency, and Consumer Trust: The Mediating Role of Perceived Authenticity in Perceptions of AI-Generated FMCG Marketing in France

1. INTRODUCTION

1.1. Overview

AI is changing marketing practice by enabling companies to design customised, scalable, and rapidly adapted promotional content. Additionally, AI is utilised in digital marketing and e-commerce, leveraging its potential to generate ads, recommendations, and promotional materials that can improve communication and interactions with customers (Saleh & Zeebaree, 2025). Consumer perceptions of the source, authenticity and motives behind marketing messages are also affected. This creates a dual hurdle for the companies that do business in digitally oriented markets: getting the efficiency boost of AI, and keeping communication channels open that consumers deem as clear and reliable. The issues are particularly relevant in France, where businesses are beginning to implement AI in their customer interactions marketing workflows (Ejjami, 2024). The value of AI-assisted marketing therefore depends not only on technical capabilities but also on how clearly brands communicate their use. Cross-national research including 1,561 adults in France found that psychological need fulfilment was associated with AI positivity and negativity across European populations, reinforcing the importance of contextual interpretation of AI attitudes (Bergdahl et al., 2023). Recent advertising research distinguishes generative-AI disclosures that communicate how content was produced from conventional disclosures that primarily reveal persuasive intent, reinforcing the conceptual separation between AIT and EAD in the present model (Dineva & Acar, 2026).

Ethical Advertising Disclosure (EAD) and AI-content Transparency (AIT) are similar but conceptually different forms of openness. EAD concerns consumers’ ability to identify promotional, sponsored, or commercial intent, whereas AIT concerns firms’ disclosure that AI was involved in creating or delivering marketing content. Perceived authenticity can be reinforced by transparent brand communication so that it lessens the uncertainty about the manner and rationale of creating a message (Yang & Battocchio, 2021). Recent research also indicates that AI disclosure may influence credibility and consumer judgement, but the direction of the relationship depends on how consumers make sense of the disclosure (Baek et al., 2026). Perceived Authenticity (PA) is therefore relevant because consumers assess the authenticity of a brand; they consider sincerity, genuineness, and consistency in determining whether they can trust the brand. In the French FMCG setting, examining these relationships may help explain how trust relates to different forms of transparency.

1.2. Problem Statement

The increasing use of AI in marketing poses a trust challenge to FMCG brands because consumers may not easily determine promotional intent or how AI was used to create a message. Limited disclosure may be associated with uncertainty about authorship, persuasion, and potential manipulation, which may correspond with greater scepticism towards the communication (Saleh & Zeebaree, 2025). Whether consumers receive credible information that enables them to understand the message and the brand’s intentions is a central question. Low levels of transparency can undermine perceived authenticity in situations where consumers doubt whether communication is based on authentic brand values or on autopilot persuasion. The commercial value of this concern is that trust facilitates long-term relationships with consumers, and perceived deception can undermine credibility and brand image (Ejjami, 2024). FMCG companies thus require evidence on whether advertising disclosure and AI-content transparency have a direct or indirect relationship with trust via authenticity or both.

1.3. Research Gap

The field of AI marketing research has grown significantly but there are still key conceptual, contextual, and methodological gaps. Previous research has tried to analyse disclosure, authenticity, credibility, or trust separately, and the nature of interactions among disclosure cues is also not well understood in previous research. More recently, however, there is no consistent pattern of beneficial responses to AI disclosure: AI disclosure can help to build trust and transparency in some contexts, but can also lead to mistrust when consumers think poorly of AI participation (Kirkby et al., 2023). Likewise, (Baek et al., 2026) reveal that AI disclosure is linked to advertising evaluations via the credibility-related judgements, suggesting that transparency is not necessarily advantageous. Research has been mainly focused on digital advertising in general, and a smaller number of studies have been conducted regarding online FMCG consumers in France (Ejjami, 2024). In addition, perceived authenticity is often considered an outcome of disclosure indicators instead of a statistical pathway connecting disclosure indicators and trust (Majerova et al., 2025). This study fills these gaps by distinguishing between EAD and AIT and exploring PA as a mediator in a single signalling-based model. A recent systematic review further identifies trust, security, personalisation, convenience, and effectiveness as recurring dimensions of the AI-consumer relationship, supporting closer examination of trust-related mechanisms in brand contexts (Ribeiro et al., 2025).

1.4. Purpose Statement

The paper examines the link between ethical advertising disclosure and the transparency of AI-generated content and consumers’ trust in the general perception of AI content in FMCG marketing, with perceived authenticity as a mediator. It also examines the effectiveness of the signalling theory in explaining consumer behaviour with regard to two transparency signals: commercial intent disclosure and AI involvement disclosure. The study distinguishes these signals and examines whether their associations with trust are direct, indirect through authenticity, or both.

2. LITERATURE REVIEW

2.1. Theoretical Foundations: Signalling Theory and Persuasion Knowledge

Signalling theory originated in economics to explain how observable cues reduce information asymmetry between parties with unequal information (Spence, 1978). In marketing, the theory has been extended to explain how firms communicate less observable qualities such as reliability, integrity, and credibility (Bafera & Kleinert, 2023). This theory applies to AI-assisted advertising because, in many cases, consumers cannot independently determine who produced a message, why it was produced, or how much AI was used. EAD may signal openness to persuasive intent, whereas AIT may signal openness to content provenance and technological participation. When perceived as credible, disclosure cues can reduce information asymmetry and strengthen perceptions of brand transparency, although their effects may vary with consumers’ existing perceptions of brand credibility (Sansome et al., 2026). Signalling theory thus explains how observable disclosure signals can convey information about less observable attributes of the firm and its disclosure.

Persuasion Knowledge Theory provides a complementary explanation of how consumers respond when persuasive intent becomes salient. Instead of believing that disclosure is automatically more likely to raise the level of trust, this view suggests that consumers apply their awareness of persuasive techniques to judge the intentions of advertisers and determine whether to question or oppose the message. (Van Reijmersdal et al., 2016) found that sponsored-content disclosures can activate recognition of persuasive intent and resistance strategies. (Wojdynski & Evans, 2016) also demonstrated that disclosure position and wording affect recognition of native advertising, whereas (Hwang & Jeong, 2016) found that disclosure of sponsorship affects responses to sponsored communication. In the current context, EAD can make persuasive intent more salient, whereas AIT can prompt consumers to consider the technology used to create the message. Experimental evidence also indicates that AI disclosure labels can shape advertising and brand attitudes through inferences of manipulative intent, supporting the relevance of persuasion knowledge in AI-mediated advertising (Wortel et al., 2024).

These perspectives are complementary rather than contradictory. Signalling theory describes why EAD and AIT can lessen information asymmetry by observable signals about commercial intent and technological authorship, and persuasion knowledge describes how consumers process those signals when persuasive intent is activated. The primary framework is signalling theory, while Persuasion Knowledge Theory helps explain why disclosure may also prompt critical evaluation; together, they clarify relationships between disclosure signals and trust. (Kirkby et al., 2023) found that disclosed AI authorship is not necessarily associated with lower authenticity when the communication remains credible. (Baek et al., 2026) likewise show that AI disclosure can reshape credibility-based advertising evaluations. Together, these perceptions provide a more detailed theoretical basis for the proposed relationships.

2.2. Ethical Advertising Disclosure

Ethical advertising disclosure concerns the clarity of sponsorship, promotional, and commercial intent. This paper uses the term EAD to focus on the ethical aspect of disclosure, which implies being clear and transparent, fair, and non-deceptive in relation to sponsorship and commercial motive, rather than advertising ethics in its broader context. Transparency can reduce confusion because consumers can more effectively identify the type of message and evaluate brand behaviour. (Hwang & Jeong, 2016) proved that a sponsorship disclosure changes the response to sponsored communication, and (Van Reijmersdal et al., 2016) found that a disclosure can activate persuasion knowledge and resistance strategies. These results indicate that disclosure may not always help to increase persuasion; its value varies depending on whether the openness is viewed as an indication of truthfulness or as a signal of manipulation. Newer discoveries associate credibility and perceived authenticity with greater consumer response during online advertising (Kothari et al., 2025). This difference is especially important when it comes to AI-based marketing since consumers can consider both the commercial purpose and the technology used to generate a message. The national focus on the clear identification of commercial content in digital influence also aligns with the disclosure-specific meaning in France (Aade & Goanta, 2025). The ARPP’s 2024 Responsible Influence Observatory documented continuing improvement in the transparent identification of commercial collaborations in France (ARPP, 2024). EAD is therefore interpreted here as an ethically framed advertising-disclosure construct rather than a general measure of advertising ethics.

2.3. AI-Content Transparency

AI-content transparency refers to the extent to which firms clearly explain how artificial intelligence is used to create, modify, or deliver marketing content. In contrast to traditional disclosure of sponsorship, AIT deals with the uncertainty regarding the content provenance and technological authorship. (Kirkby et al., 2023) discovered that reporting the use of an AI-generated brand voice did not always diminish brand authenticity, which implies that consumers might not reject AI intervention when reported in a transparent manner. Nevertheless, AI disclosure may also be associated with increased scrutiny when consumers perceive machine-generated content as less credible or less human in its intentionality (Baek et al., 2026; Pierre, 2025) also shows that brand transparency regarding AI may influence brand assessments, and consumer perceptions of AI are a critical boundary condition. These ambivalent results reveal that AIT cannot be presupposed to have a similar positive reaction. Its connection with consumer judgements rests on whether disclosure is taken as a sign of credible openness, a sign of technological competence, or a reason for scepticism. Recent experimental evidence shows that AI disclosures can influence trust through both conceptual AI knowledge and attitudinal persuasion knowledge, indicating that transparency may generate different trust responses depending on how the disclosure is interpreted (Koning & Voorveld, 2025).

2.4. Perceived Authenticity and Consumer Trust

Consumer trust and perceived authenticity are related but distinct consumer judgements. PA concerns whether brand communication is perceived as authentic, genuine, credible, and consistent with brand identity, whereas CT reflects consumers’ willingness to rely on the brand’s honesty and reliability. (Morhart et al., 2015) conceptualised brand authenticity as a multidimensional construct with credibility, integrity, continuity, and symbolism. Authenticity may be strengthened through open communication because transparency provides customers with the information that less responsible brands find difficult to counterfeit (Yang & Battocchio, 2021). Authenticity has also been noted as a significant interpretive route that consumers use to perceive machine-generated content based on recent research on AI (Majerova et al., 2025). Trust is consequently anticipated to be greater where the disclosure cues are linked with enhanced perceptions of genuine communication. At the same time, transparency may also be directly associated with trust, as it reduces uncertainty, and partial, but not full, mediation is theoretically feasible.

2.5. Development of the Hypotheses

The hypothesis is that ethical advertising disclosure is positively related to perceived authenticity since transparent disclosure can make the persuasive intent more apparent than having consumers guess the presence of an underlying commercial objective. Clear sponsorship and promotion disclosure can lead to less scepticism, as consumers may see such disclosure as a sign that the brand is telling the truth (Kothari et al., 2025). From a signalling theory perspective, EAD is an observable signal that can lessen information asymmetry about the motives of advertisers. (Zafar et al., 2025) also associated disclosure with perceived ethicality and trust in digitally manipulated advertising. Disclosure can enhance more honest and responsible communication by assisting consumers to realise the commerciality of a message without feeling that there is some hidden agenda. Thus, the following hypothesis is proposed:

H1: Ethical advertising disclosure is positively associated with perceived authenticity.

It is assumed that EAD is positively related to consumer trust since the disclosure of commercial practices can be a sign of honesty and procedural fairness. As (Sands et al., 2026) note, the responses to AI-generated advertisements could be partly contingent on the way consumers view the behaviour and motives of the advertiser. When the promotional purpose is clearly disclosed, consumers might not be as likely to suspect hidden persuasion and might form an opinion about the brand based on fuller information. Signalling theory suggests that EAD can decrease the ambiguity about the source intent and enable trust in marketing communication (Bafera & Kleinert, 2023). This direct route is conceptually distinct from the authenticity pathway because consumers may value openness even when authenticity judgements are only moderately related. Thus, the following hypothesis is proposed:

H2: Ethical advertising disclosure is positively associated with consumer trust.

AI-content transparency is hypothesised to be positively associated with perceived authenticity when disclosure minimises uncertainty about the production of marketing content. Customers who realise that AI was used to create content may be less likely to encounter the technological source later and interpret its earlier omission as concealment. (Kirkby et al., 2023) discovered that disclosing AI authorship does not always hurt brand authenticity, which means that transparent AI utilisation may still be in line with authentic brand communication. (Majerova et al., 2025) also link perceptions of AI-generated content to authenticity and credibility judgements. From a signalling-theory perspective, AIT can be conceptualised as a visible signal concerning content provenance and the firm’s willingness to disclose its production process. When that cue is perceived as credible, it may support perceptions of honesty rather than ambiguity. Thus, the following hypothesis is proposed:

H3: AI-content transparency is positively associated with perceived authenticity.

AIT is also expected to be positively associated with consumer trust because explicit information about AI involvement may reduce uncertainty about the origin and production of marketing content. (Pierre, 2025) shows that brand transparency concerning AI is associated with consumer evaluations, indicating that openness about technology becomes part of how consumers assess a firm’s conduct. Nevertheless, disclosure can also lead to a more critical assessment if AI is perceived negatively; i.e., the anticipated association depends on transparency being perceived as responsible communication rather than as a warning. (Baek et al., 2026) identify credibility as central to consumer reactions to AI disclosure. Signalling theory thus predicts a positive relationship when AIT provides transparent and plausible information associated with lower perceived concealment. Thus, the following hypothesis is proposed:

H4: AI-content transparency is positively associated with consumer trust.

Perceived authenticity is hypothesised to be positively associated with consumer trust, as genuine communication may indicate consistency between what a brand communicates and how it behaves. Customers are more likely to trust brands whose messages seem genuine, authoritative, and consistent with an existing identity. As (Yang & Battocchio, 2021) discovered, authenticity can be enhanced through open communication and, consequently, build trust-related responses. (Majerova et al., 2025) also identify authenticity as an important evaluation in consumer reactions to AI-generated content. From a signalling-theory perspective, PA reflects the consumer’s interpretation that the available cues are credible rather than opportunistic. Such an interpretation may be associated with less uncertainty about brand motives and a greater likelihood of trust. Thus, the following hypothesis is proposed:

H5: Perceived authenticity is positively associated with consumer trust.

PA is hypothesised to mediate the association between EAD and CT because commercial disclosure may be associated with perceptions of authentic, responsible communication. According to (Sands et al., 2026), judgements of advertiser motives and behaviours inform consumer reactions to AI advertising. When EAD signals openness, consumers may perceive the brand as more genuine, which may be associated with greater trust. This statistical pathway is consistent with signalling theory because disclosure is observable, whereas authenticity represents the consumer’s interpretation of the less observable quality signalled by that disclosure. The model would thus anticipate EAD to relate to CT directly and indirectly via PA instead of considering authenticity to be the sole channel. Thus, the following hypothesis is proposed:

H6: Perceived authenticity mediates the association between ethical advertising disclosure and consumer trust.

PA is also hypothesised to mediate the association between AIT and CT because disclosure of AI involvement may be associated with perceptions of technologically assisted marketing as transparent and authentic. (Majerova et al., 2025) demonstrate that authenticity and trust are the key aspects of consumer assessments of AI-generated content. (Kirkby et al., 2023) also show that disclosed AI authorship need not reduce authenticity when communication remains credible. In signalling theory, AIT may be associated with less uncertainty regarding the provenance of the content, and PA reflects the belief of the consumer that such transparency indicates honest brand behaviour. Trust may therefore be higher when AI disclosure is associated with greater authenticity, while a direct transparency-to-trust path may remain. Thus, the following hypothesis is proposed:

H7: Perceived authenticity mediates the association between AI-content transparency and consumer trust.

2.6. Research Model

Fig. (1) summarises the hypothesised associations among EAD, AIT, PA, and CT.