Human or Machine? Exploring Gen Z’s Perceptions of Authenticity in AI-Driven Marketing

Prepared by the researche  :

Samir Meradi^٭1 & Samira Salah² & Soumia Abdelhak³

• ^1&3 Djillali Liabes University, Sidi Bel Abbes, Algeria
• ² University of Oran 2 Mohamed Ben Ahmed, Oran, Algeria

DAC Democratic Arabic Center GmbH

Arabic journal for Translation studies : Fourteenth Issue – January 2026

A Periodical International Journal published by the “Democratic Arab Center” Germany – Berlin

:To download the pdf version of the research papers, please visit the following link

ORCID iD 1 : 0000-0003-3308-3204

ORCID iD 2 : 0009-0001-4161-0804

ORCID iD 3 : 0009-0009-3999-3285

Introduction

Recently, digitalisation has transformed nearly every facet of marketing, encompassing content creation and consumer interaction (Cioppi et al, 2023). With brands utilising digital channels increasingly, they have consequently implemented digital strategies that enhance customer communication, including resorting to the power of analytics in order to help personalise approaches and optimise the customer experience (2023). Digitalisation represents the integration of digital technologies into everyday life and business activities, resulting in the change of organizational processes, consumer experiences and new market offerings (Calderón-Monge & Ribeiro-Soriano, 2024); (Artusi, Snihur, & Marullo, 2024). This shift in marketing led to the creation of new tools, platforms, and techniques that aim at creating compelling and interactive brand experiences (Lemon & Verhoef, 2016). Content marketing is a foundational aspect of digital marketing, signifying the practice of intentionally and consistently creating and sharing relevant and valuable content to attract, acquire and engage a clearly defined and understood target audience – to drive profitable customer action (Pulizzi, 2014). Unlike traditional methods of advertising content marketing places emphasis on telling a story, evoking an emotional response, and educating customers. It is particularly beneficial in addressing the problems of receiver overloading and consumer wear-out of traditional advertising (Dessart, Veloutsou, & Morgan-Thomas, 2019); (Gowda & Archana, 2024). The rise of generative AIs is a step forward in content creation. GenAI refers to the artificial intell i genc e systems which can create “new” data, suchas text, images, v ideo or audio content, by using large datasets and methods of machine learning (Heigl, 2025). Tools like Marketers, for text, Midjourney, for photos and Sora, for video, are already using ChatGPT to automate and personalise creative production (Heitmann, 2024). This has reignited debate around authorship, novelty, and affective content in computer-generated work (Smith & Hutson, 2024). This research explores the concept of perceived authenticity that refers to the subjective judgment that the consumers develops regarding the truthfulness, genuineness and cultural/historical match of a product, experience or practice (Kolar & Žabkar, 2010). It results of subjective judgment following on perceived trustworthiness and consistency of expectations against experiences. The perception is socially constructed through the compromise between the objective features of the offer and recipients’ subjective interpretation (Napoliet al, 2014). Authenticity is becoming increasingly important to online consumers, particularly Generation Z, who oftentimes prefers raw, relatable, and humanized content to polished, overly produced corporate messages (Seemiller & Grace, 2019). According to recent studies, brand authenticity is an important predictor of brand warmth, experience, and trust (Safeer, He, & Abrar, 2021); (Deng, Wang, & Li, 2024). Relevant in this context is narrative engagement, or narrative transportation, which is associated with a psychological state in which persons are mentally and emotionally immersed in a narrative (Sestir & Green, 2010). The persuasive effect, emotional intensity, and memory of a story with high involvement of the audience are higher (far) than those of one with low involvement (Green & Appel, 2024). However, it is unknown whether AI-authored stories may be able to achieve the levels of engagement, and emotional authenticity of human-authored stories, such as user testimonials or brand stories (Wahid et al, 2023). In light of the growing application of generative artificial intelligence (GenAI) in brand storytelling, research is needed to understand how young consumers appraise, evaluate and emotionally respond to AI-produced narrative content as opposed to the same content produced by a human. This article studies Generation Z as a particularly digitally literate cohort, and a group with critical attitudes to the corporate communication content they consume, alongside a strong desire for authentic, creative and emotionally relatable material. Seemiller and Grace (2019). However, digitalisation and the rise of generative AI tools have profoundly altered the marketing landscape, notably in branded storytelling and content creation. On the other hand, such rapid development has raised crucial questions in terms of their efficiency and popularity from a user perspective. The increasing dependence on AI-produced content to elicit emotional engagement raises important issues with the perceived authenticity, trustworthiness, and narrative coherence of this content (Huschens et al., 2023); (Limantara, 2024). Despite huge developments in AI generative technologies and their ability to help brands ‘write’ compelling narrative content, consumers remain suspicious of machine-generated materials, particularly when it comes to emotionally engaging storytelling or the personal endorsement of a product. This scepticism raises a critical issue on the importance of authenticity in marketing and the potential gap between AI-made narratives and real-life human experiences. To surf on this vast wave is a challenge, as brands should blend between state of the art technology and transparent, understandable and approachable conversations with its audience. Studies show that consumers often view AI-derived content as less credible and express concerns for its emotional richness and authenticity when compared to human-authored narratives (Kirk & Givi, 2024); (Belliset al, 2024). Additionally, brand authenticity is also an important driver of consumer contact and trust, especially among millennials who value the authenticity and ethical agreement of brand messages. The increasing use of AI in marketing raises crucial questions about the maintainance of the perceived truthfulness of brands in consumer engagements, particularly in the context of consumers’ increasing desire for ethical and transparent business practises (Wen & Laporte, 2025). New studies show that generative AI can dramatically improve the efficiency of marketing content creation as well as enable deep personalization. AI has been used for optimal creation of content, thus reducing time and resource consumption (Potwora, et al, 2024); (Tang, 2024). Alternatively, it permits tailored messages to engage and be relevant to individual consumers, seem to be important to consumers (Gungunawatt et al, 2024); (Gao & Liu, 2023). Yet, for all this benefit, there are those who warn of the potential drawbacks, referencing the risk of disconnection (emotional distant, perceived “manipulation” or ethical ambiguity in AI-generated marketing materials) (Leib et al, 2021). Academics echo this concern, describing how if AI becomes too personal (i.e., hyper-personalised), it can lead to a loss of authenticity and trust, especially when users do not know AI is being used in a particular decision-making process. The use of persuasive algorithms poses ethical implications with regard to consumer autonomy and transparency and could lead to reputation damage for firms in the long run. These gaps highlight a lack of understanding in how digital consumers specifically Gen Z digital consumers- evaluate AI-synthesized content in terms of utility and emotional, ethical, and sensory attributes. The lack of empirical data about how the current generation perceives the believability, emotional attachment and authenticity of AI-created stories calls for a deeper qualitative inquiry.

Given the growing use of generative artificial intelligence (GenAI) in marketing and the lack of empirical evidence on how young consumers perceive such content, this study seeks to address the following research question: How do Generation Z consumers perceive and evaluate AI-generated versus human-created brand content, particularly in terms of authenticity, emotional engagement, and credibility?

From this main research question, arise the following sub-questions, which aim to explore its various dimensions in more depth:

RQ1a: How do these perceptions influence emotional connection, trust, and narrative attachment toward the brand?

RQ1b: How does the type of narrative format (blog posts, social media images, customer testimonials) affect perceived authenticity and engagement?

RQ1c: What ethical reflections or concerns emerge regarding transparency and the disclosure of AI use in branded storytelling?

The aim of this paper is to explore Generation Z’s attitudes toward AI-generated versus human-created advertising content, with a particular focus on authenticity, emotional expressivity, and credibility. The study seeks to contribute to ongoing academic discussions about the role of generative AI in content marketing, while offering reflections on the evolving expectations of a generation raised in a highly digital environment.

Specifically, it examines how digitally native consumers perceive and emotionally respond to marketing stories co-created by AI and by human creators, and how these perceptions differ across three content formats: blog posts, social media images, and customer testimonials.The goal is to understand the subtle nuances of perceived authenticity, emotional connection, and narrative credibility within these two modes of storytelling. By doing so, the study aims to enrich the emerging literature on human–AI collaboration in marketing and to provide actionable insights for content creators, marketers, and brand managers seeking to engage younger consumers in more authentic and emotionally resonant ways.

Theoretically, this study contributes to the understanding of authenticity and emotional engagement in AI-generated storytelling, an area still at an early stage of academic inquiry. From a managerial perspective, it offers practical guidance for brands on how to use AI creatively yet transparently to enhance consumer trust and preserve the human dimension of digital communication.

Our research is of a qualitative and exploratory nature, grounded in the interpretivist (constructivist) paradigm. Considering the study’s purpose to understand subjective perceptions and affective reactions, as well as authenticity judgements of AI-established versus human-created marketing content, a constructivist approach was believed to be appropriate. According to Evrard, Pras, and Roux (2009), in the interpretivist paradigm meaning was not directly transcendent but, constructed collectively between the researcher and subjects, hence, is suitable method in the studying complex situation based on human experience and social contexts

1. Methodology

I.1. Research Paradigm

The study adopts an interpretivist paradigm, in which reality is understood as a product of individuals’ subjective experiences, interpretations, and interactions with their environment. Rather than seeking to establish objective truths, this approach aims to understand how participants construct meaning around AI-generated marketing texts. It is particularly suitable for exploring perceptions of authenticity, emotional impact, and trust within computer-mediated communication. As Evrard, Pras, and Roux (2009, pp. 29–30) emphasize, interpretive perspectives in marketing do not aim to generalize findings but rather “to achieve a rich and distinctive understanding of phenomena in their context.”

I.2. Context and Research Design

Given the emerging nature of generative AI in marketing, an exploratory qualitative design was adopted to gain an in-depth understanding of how individuals perceive and emotionally respond to AI-generated content compared to human-created material. This design allows the generation of new insights in an inductive, data-driven manner rather than testing pre-formulated hypotheses.

Data were collected through semi-structured interviews, allowing for a balance between guided discussion and open exploration of participant perspectives. A theme-based interview guide was developed, covering core constructs such as digital content practices, perceived authenticity, emotional engagement, and ethical reflection.

I.3. Participants

Participants were recruited using purposive sampling from Generation Z (aged 18–26), known for their high digital literacy and frequent engagement with online marketing content. Recruitment was carried out via targeted social media outreach and academic networks. The final sample consisted of 17 participants (both genders) residing in Algeria.

The sample size was determined based on data saturation interviews continued until no new information or themes emerged, ensuring both depth and representativeness of insights (Guest, Bunce, & Johnson, 2006).

Table 1 below details the demographic characteristics of the sample, including participants’ gender, age, and education level.

Table n°01: Profile of Participants

The stimuli were pre-tested and validated by two marketing communication experts to ensure realism, coherence, and clarity. Images were embedded directly into the interview guide, while videos were shared via secure hyperlinks for accessibility.

I.4. Research Design

The stimuli used in the interviews represented five thematic dimensions of AI-generated versus human-created content.

• Perceived Authenticity: examined the extent to which the content appeared genuine, human-like, and emotionally real.
• Credibility and Trust: focused on participants’ perceptions of reliability and confidence in the message and its source.
• Emotional Impact and Engagement: explored how the content captured attention and elicited emotional reactions or empathy.
• Narrative Attachment: assessed participants’ level of immersion and emotional connection with the story presented.
• Ethical Reflection: addressed moral and transparency concerns regarding the use of AI in branded storytelling.

Table n°2: interview themes table

Source: Elaborated by authors

I.5. Procedure

Interviews were done in-person, individually, or online, based on participant availability.  Depending on participant option, each session was conducted in either Arabic or French and lasted between 30 to 45 minutes.

 Three primary steps were taken in the process:

 Introduction: The aim of the study and the fact that participation was voluntary were explained to the participants.

 Exposure: Marketing materials produced by AI and humans were displayed to the participants. Participants responded to each stimulation by sharing their ideas, feelings, and moral considerations.

After obtaining consent, all interviews were audio recorded and verbatim transcribed for analysis.

1.6. Ethical Considerations

Ethical protocols were rigorously followed. Participants provided informed consent, were assured of confidentiality, and were assigned coded identifiers (e.g., P1, P2). The study complied with institutional ethical guidelines and received approval from the Ethics Committee of Djilali Liabès University. Participants were informed of their right to withdraw at any time without penalty. Data were securely stored and used exclusively for academic purposes.

1.7. Data Analysis

Transcribed interviews were analyzed using reflexive thematic analysis (Braun & Clarke, 2006), supported by NVivo software. The process followed six stages: familiarization, coding, theme development, review, definition, and interpretation.

The analysis focused on identifying patterns of meaning in participants’ narratives concerning authenticity, emotional engagement, credibility, and ethical perceptions. Both inductive coding (emerging from data) and deductive interpretation (guided by theoretical constructs) were employed.

8. 8. Methodological Rigor

To ensure credibility, reliability, and transparency, several quality criteria were applied:

• Credibility: achieved through participant quotations and triangulation of stimuli (visual, textual, video).
• Audit trail: maintained by documenting all analytical decisions and coding processes.
• Reflexivity: ensured through a researcher’s journal capturing assumptions and interpretive reflections.
• Peer debriefing: two academic colleagues reviewed a subset of transcripts and codes to validate interpretive consistency.

These procedures collectively reinforced the trustworthiness and rigorous interpretive quality of the study.

1. Results and discussion

This section presents the main themes of the content analysis organized according to these five categories: Perceived Authenticity, Credibility and Trust, Emotional Impact and Engagement, Narrative Attachment, and Ethical Reflection. These themes represent how consumers cognitively and emotionally respond to human vs. AI-authored marketing content and are related to authenticity, trust, emotional connection, narrative affiliation, and ethical considerations. Participant quotes are provided to supplement each theme for a more enlightened analysis.

II.1. Perceived authenticity

The handwritten brand image was viewed as more genuine than the AI-generated one by the vast majority of participants (about 14 out of 17). Emotional salience, anthropomorphism, visual realism, and the apparent human work and care that went into the product were the foundations of this prevailing view.

Respondents frequently around 15 of them said the photo that was made by hand “looked more real,” and provided a “human touch,” a “connections” that was stronger because of its natural light, texture, and imperfections perceived marks of authenticity.

“…The handmade brand image feels more authentic because it looks more real and natural, not digitally created. It gives a sense of personal effort and craftsmanship that AI-generated images often lack…” (Student, 21)

“…Looking at the images, the authentic handmade brand image definitely feels more real and genuine. There’s something about the imperfections and texture that gives off a human touch…” (Student, 22)

The AI-generated picture, on the other hand, was described by most respondents about 12 participants as ‘too perfect,’ ‘too cold,’ or ‘too empty things that betrayed its supposed authenticity. They said that even though the AI version was technically perfect, it seemed soulless and preplanned, making the scenes come off as fake or rehearsed.

“…The AI one looks polished, but almost too perfect, like it’s trying too hard and it looks cold…” (Student, 22)

“…The AI-generated image looks fake and too perfect, like it’s silent or empty…” (Student, 22)

These judgments relied on aesthetic cues. Nearly all participants about 15 out of 17 used cues such as shadows, lighting, composition, and color saturation to make attributions of human intention  and environmental realism. The crafty-looking image was lauded majority around 13 participants for expressing a real-life sense of mood and/or context, occasionally triggering connotations such as seasonal exuberance or even a certain way of life.

“…The natural background and the shadows reflected on the table suggest the presence of sunlight… The image also gives me a sense of safety and trust toward the product and the brand…” (Student, 23)

“…The image gives an energy of summer and of a beautiful day, the energy of having fun and enjoying the moment… The AI one feels staged or artificial without any energy…” (Student, 22)

Several participants around one-third of the sample made comments that the handmade image led to better emotional and cognitive engagement because it was more memorable and relatable, and more expressive of the brand’s values. The imperfections and personal mark of the image were presented not as flaws, but as proof of sincerity and meaning.

“…You can sense the love and care that went into creating it, which makes it more meaningful and genuine…” (Student, 24)

“…The handmade branding gives off a vibe that real people care about what they’re making…” (Student, 22)

A small minority (about three participants) expressed a slightly different view, noting that the AI-generated image was aesthetically appealing and professionally refined, though still lacking emotional warmth.

Overall, perceived authenticity as a construct seemed to be steeped in strong emotional and sensory experiences, not so much related to how well defined or resolution qualities in the image or story, but rather the story and labor of humanity the audience perceived behind the images. The AI michelle the image – even with all the hi tech, was regarded as cold and ‘unfeeling’ in terms of representing a brand identity and human emotion.

In relation to RQ1 and RQ1b, these findings suggest that Generation Z consumers evaluate AI-generated and human-created brand visuals through the lens of emotional authenticity rather than technical quality. The results demonstrate that perceived authenticity is closely tied to human imperfection, emotional realism, and effort — elements that drive deeper engagement and trust. This supports the idea that, for young consumers, authentic brand storytelling emerges not from digital perfection but from the visible and emotional trace of human presence behind the message.

II.2. Credibility and Trust

Participants were significantly more trusting of and attributed greater credence to human-written content compared with content generated by AI. This was the view of the vast majority about 15 out of 17 participants. One recurring justification was that human-authored messages expressed lived experience, emotional resonance, and an authentic communicative intent that AI-generated messages did not.

   ‘…The human testimonial seems more credible because it shows a real experience of people who actually used the product or the service… I can’t fully rely on what [AI] says…” (Student, 22)

‘…The human-written text seems more credible because it has a soul and it comes from the heart… with just enough words used to give an idea to the reader…” (Student, 22)

hile AI-generated content was generally regarded by most respondents around 13 participants as well-organized and grammatically correct, many emphasized that it often lacked an appropriate tone, sentiment, or displayed signs of over-commercialism. This generated skepticism and reduced trust toward brands relying heavily on automation in their communication strategies.

‘…Yes, it affects my trust because I feel like the content is fake and there is no effort or real emotions put into it…” (Student, 21)

“…When a company relies only on AI, it feels like they didn’t make the effort to speak about their product themselves…” (Student, 24)

ansparency and intention were recurring themes among several respondents (about one-third of the sample) when evaluating the use of AI. Although a few participants (around 4 out of 17) were open to AI-generated content if its use was clearly disclosed, the majority approximately 13 participants associated credibility, authenticity, and loyalty with human-written messages.

“…If I find out a brand uses AI for everything, I’d question if they even care about connecting with people. However, if they are upfront… maybe it is okay. Hiding it feels sketchy…” (Student, 23)

   “…Knowing something was written by AI doesn’t automatically make me distrust the brand… but I do tend to trust brands more when I know a real person is behind the message…” (Student, 21)

One or two participants held a different perspective, suggesting that AI-generated text could appear more credible due to its clarity, structure, and neutral tone. However, the overwhelming sentiment across the group was that true credibility derives not only from coherence or grammar, but from perceived human intent, emotional depth, and care — qualities participants felt that only humans can genuinely convey.

“…The first text seems more credible to me because… it feels like it was written with care and intention by someone who understands the topic…” (Student, 22).

Overall, these findings support RQ1a and RQ1c by showing that trust and credibility judgments among Generation Z depend strongly on perceptions of human intention and transparency. This aligns with existing literature emphasizing that brand credibility is not solely linguistic or structural, but experiential and affective, rooted in perceived honesty and human connection (Lemon & Verhoef, 2016; Wen & Laporte, 2025).

II.3. Emotional Impact and Engagement

Throughout participants’ answers, the human-made video evoked stronger emotional engagement compared to the AI-generated video. This observation was shared by nearly all participants around 15 out of 17. The discrepancy was based on perceived realism, emotional depth, sensory richness, and the sense of human presence behind the image and the voice.

The visual and acoustic signs including authentic ambient sound, human voice-over, and natural animal movement were identified by the majority of respondents about 14 participants as powerful emotional triggers and empathy enablers.

      “…The human video affects me more because it’s smooth and relaxing. It’s nice to watch and makes me feel calm, which helps me connect with it more…” (Student, 21)

     “…Watching the animals, insects and birds in their natural environment: eating, hunting, just living their lives felt real and immersive…” (Student, 22)

For most participants around 13 out of 17, emotional realism depended on the perceived human effort and intention behind the work. Knowing that a person had filmed the scenes with patience and care gave the content additional emotional weight and memorability..

“…here’s something deeply grounding in knowing that a human was behind the camera, patiently waiting for the perfect shot…” (Student, 23)

The AI-generated video, in contrast, was criticized by a large majority about 12 participants) as having an emotional response that was “flat,” “unfelt,” or “unnatural.” While technically impressive, it failed to create a tactile or emotional connection. Common criticisms included artificial movement, lack of sound, and a rehearsed feeling.

     “…The AI-generated video is silent. Even though it looks real, something is missing—it feels empty, like it has no emotions…” (Student, 22)

“…The dogs moved in a strange, unnatural way, which made it feel fake and a bit distracting…” (Student, 21)

A small minority (2 or 3 participants) appreciated the AI video for its novelty and technical sophistication, but still found it emotionally detached.

“…Even though the OpenAI Sora video with the snow dogs was intriguing… it didn’t leave the same emotional impact…” (Student, 24)

Beyond visual content, participants responded to a symbolic and emotional dimension—linking the human-crafted video to realness, care, and expressive intent, while the AI-based one was perceived as soulless or mechanical.

“…It’s simply human I can feel the connection and it’s addressing me in my own language. It’s realistic, vivid, and full of life…” (Student, 22)

“…A human-made video is more emotional. I can visually see the life in that jungle and even hear it, while in the AI-made one, the puppies look fake…” (Student, 23)

Ultimately, emotional engagement emerged as a multisensory, value-driven response, shaped by realism, human agency, and expressive imperfection. Although visually refined, the AI-generated video lacked emotional texture, thereby diminishing its empathic resonance. In contrast, the human-created video’s minor flaws and spontaneous moments were seen as authentic emotional cues, reinforcing connection and trust toward both the content and the sponsoring brand.

“…Of course the human video, because it is real—and nothing is better than God’s creation. Subhan Allah…” (Student, 24).

In connection with RQ1b, these findings demonstrate that emotional impact and engagement are strongly mediated by perceived human involvement and sensory realism. Generation Z participants were more emotionally transported by narratives that conveyed effort, imperfection, and human intent, echoing theories of narrative attachment and authenticity in the literature (Sestir & Green, 2010; Green & Appel, 2024). This suggests that while AI-generated media can achieve technical excellence, it still struggles to reproduce the emotional richness and authenticity necessary for deep consumer engagement.

II.4. Narrative attachement

Brand narratives that were told or co-generated by humans received higher levels of emotional and engagement-based evaluation from the vast majority of participants (around 14 out of 17, representing approximately 82%), compared with entirely AI-generated stories. This attachment was grounded in a sense of reality, emotional richness, and a feeling of proximity to genuine human experience.

Respondents repeatedly emphasized that the human-made story was “more believable,” “grounded in real experience,” and conveyed “true emotion” that allowed them to feel connected to the brand. About 13 participants mentioned that authentic testimonials “feel like someone telling something they lived through” and that such accounts “help build trust because it’s someone like me telling it.”

“…The human-made story is more believable and engaging. It’s based on real feelings…” (Student, 21)

Meanwhile, the AI-generated tales, though sometimes well-constructed and linguistically fluent, were described by most participants (around 12 out of 17) as emotionally flat or overly polished. Several said they found AI narratives “a bit too smooth,” “algorithmically generated,” or “lacking something human,” which weakened their believability and emotional resonance.

“…It sounds good, but it’s just words no real emotion behind it…” (Student, 21)

Visual storytelling also emerged as a significant factor for narrative attachment. Around 10 participants noted that human-told narratives were accompanied by vivid mental imagery and natural emotional expression, allowing them to “see and feel” the story. The perceived authenticity and emotional involvement of the storyteller enhanced the brand’s legitimacy.

“…The visuals in the human video make the story more engaging it feels real…” (Student, 23)

A small number of participants (about 3 out of 17) acknowledged that advanced AI tools could closely approximate human storytelling, especially when modeled on real events. However, even among these respondents, the consensus was that “something is always missing” a sense of depth, immediacy, or subtlety arising from lived human experience.

“…You can tell when it’s just a smart script, not a real memory…” (Student, 24)

Emotional realism, expressive clues, and the perception of human presence all served as the foundation for the multifaceted construct that was narrative attachment.  Participants frequently believed that AI’s emotional authenticity and narrative immersion were limited by its lack of lived experience, despite the fact that AI can replicate storytelling.

 These results imply that when participants believe that a tale is based on human intent and lived experience, emotional connection, trust, and narrative attachment are substantially stronger in relation to RQ1a.  According to theories of authenticity and narrative transportation (Sestir & Green, 2010; Green & Appel, 2024), this suggests that Generation Z consumers are more interested in stories that convey genuine human emotion and experiential truth than in ones that are precisely calculated by algorithms.  These findings support the idea that emotional credibility and authenticity are still key components of brand storytelling efficacy in the AI era.

II.5. Ethical Reflection

Transparency about any use of AI-generated work was broadly regarded by the vast majority of participants (around 13 out of 17, approximately 76%) as a core ethical responsibility. The clarity over whether or not content—specifically images or videos had been generated by AI was cited by many as a direct indicator of a brand’s honesty and trustworthiness.

Participants repeatedly emphasized that AI-generated content is not inherently negative, but that transparency fosters consumer trust and reflects a brand’s respect for its audience. Concealing the use of AI was commonly viewed as potentially deceptive, especially when emotional engagement was involved.

     “…If I found out later that something I connected with emotionally was made by AI and the brand didn’t say so, I might lose trust…” (Student, 22)

The effect of AI disclosure appeared to vary depending on content type and product category. About 10 participants indicated that they were more tolerant toward AI-authored textual content, yet expressed greater concern when visual content (images or videos) was involved particularly in industries requiring high authenticity, such as skincare, food, or artisanal products.

      “…If the pictures or videos are AI-generated, it could confuse customers. It would affect my decision more than just AI-written text…” (Student, 24)

“…For ethical products, I’d rather support a brand that’s transparent than one hiding behind AI…” (Student, 22)

This ethical consideration also extended to consumer decision-making, especially in theevaluation of alternatives. About nine respondents mentioned that discovering a brand’s concealed use of AI could lead them to prefer human-made or transparent competitors.                                “…In the decision-making process, I’d be more driven toward the human option if I sensed dishonesty…” (Student, 23)

However, a small minority (about 3 participants) expressed a more pragmatic stance, suggesting that content quality could sometimes override concerns about its origin, provided the message was clear and not misleading.

“…If the AI content is convincing and still delivers, I might appreciate the innovation and still buy the product…” (Student, 21)

Despite these nuances, the dominant view (around 14 participants) was that consumers have a right to know whether the brand content was created by humans or AI. For them, this demand was not limited to authenticity, but extended to broader ethical principles such as integrity, consent, and agency in digital communication.

“…Secrets make me wonder what else they’re hiding. I want to know if I’m talking to a human or a robot…” (Student, 24)

Ethical debate over AI-generated content has focused largely on consumer rights, consented use, and brand transparency. In general, consumers were open to content creation being assisted by AI, however, they required full disclosure of the AI’s involvement. In the absence of transparency, we tend to mistrust it, especially when the context is one of emotional connection or the product involves strong ethical or personal values. In general, consumer rights, informed permission, and brand transparency were at the centre of the ethical discussion around AI-generated content.  Participants were receptive to the use of AI as a creative tool, but misinformation led to mistrust, particularly when the message or product had moral or emotional meaning.

 Regarding RQ1c, these results show that Generation Z’s assessment of AI-generated marketing content is heavily influenced by ethical considerations and transparency standards.  For this group, authenticity includes the moral integrity of the brand’s communication strategies in addition to aesthetics and realism.  This supports earlier research on digital transparency and ethical branding (Wen & Laporte, 2025; Leib et al., 2021), demonstrating that revealing AI involvement is a strategic trust-building tool that is necessary for sustaining customer loyalty rather than just a compliance gesture.

1. Conclusion

This study aims to provide some insights into whether consumers distinguish between brand content produced by humans and content written by AI, and how they respond to them by considering perceived authenticity, narrative attachment, and ethical reflection. Practical implications: The results provide useful implications for when emotional, cognitive and ethical dimension of consumer reactions to AI-based branding are concerned.

It is found that human-generated content is generally perceived to be more authentic by consumers. Natural imperfection, emotional expression, and evidence of human work are part of a greater realism and authenticity. On the other hand, though AI-made visuals or narratives are very impressive technically, they are often considered somewhat slick, emotionally uninspiring, or unable to provide the subtle clues that indicate real human participation.

Narratively, for emotional engagement consumers reported more emotional attachment to true stories. Stories delivered from a human perspective were judged as being more engaging, identifiable and believable. AI-generated stories can be immersive, but generally struggle to capture the emotional weight and nuance of human expression. Somehow consumers intuitively know this and therefore prefer all but authentic content that reflects real life.

From the ethical point of view, transparency was considered to be a major issue. Overwhelmingly, consumers favored brands indicating that AI was used in creating the ads. When such information is kept concealed, it creates doubts over the brand’s integrity, eroding trust—overshadowing more intangible product categories for which ‘authenticity’ and ‘ethics’ count for everything. Conversely, if AI usage can be openly discussed and does fit within brand values, it can be accepted — indeed valued — as sign of innovation.

In general, according to the study, brands need to responsibly integrate AI to ensure that innovation does not act against trust and emotional connection. Staying true, promoting impactful stories, and being honest about the use of AI are key ingredients in the recipe to capturing the hearts of consumers and brand fans in the AI era.

Theoretical Implications

This research contributes to the growing literature on AI-driven content marketing and brand authenticity by extending understanding of how digital-native consumers — particularly Generation Z — perceive and interpret AI-generated storytelling. It provides empirical evidence that perceived authenticity and emotional resonance remain key mediators of consumer trust even in technologically advanced contexts. Moreover, the study reinforces theoretical perspectives from narrative engagement theory and authenticity frameworks (Kolar & Žabkar, 2010; Green & Appel, 2024) by showing that emotional depth, human imperfection, and communicative intent constitute central elements in perceived realness. Finally, it enriches the emerging debate on AI ethics in marketing, suggesting that disclosure and transparency are intrinsic to perceived moral legitimacy.

Managerial Implications

From a managerial standpoint, the findings emphasize that AI should be used as a creative enabler, not a human substitute. Marketers are encouraged to maintain a human presence in AI-assisted narratives — for example, by blending algorithmic precision with human storytelling. Brands should also disclose AI involvement openly to avoid eroding consumer trust. Practitioners are advised to emphasize authentic, emotionally rich storytelling and to design campaigns that balance innovation with sincerity. Particularly for industries where authenticity is core (e.g., food, cosmetics, or crafts), transparency about AI use can become a competitive differentiator, reinforcing brand credibility and ethical reputation.

Limitations

This study is exploratory and based on a qualitative sample of 17 Generation Z participants in Algeria, which limits the generalizability of results to broader populations. The reliance on self-reported perceptions also introduces potential biases, as participants may idealize authenticity or underestimate AI’s evolving capabilities. Furthermore, the study focused on visual, textual, and narrative stimuli in specific contexts; future research might consider cross-cultural comparisons or alternative content formats (e.g., immersive or interactive AI-generated media).

Future Research Directions

Future studies could adopt mixed or quantitative approaches to validate these findings on a larger scale and measure the impact of AI disclosure on consumer trust and loyalty. Another promising direction would be to examine how cultural context and digital literacy moderate perceptions of AI-generated content. Longitudinal research could explore whether attitudes evolve as consumers become increasingly exposed to AI co-created materials. Finally, integrating neuroscientific or biometric methods could offer deeper insights into the emotional and cognitive processing of AI versus human content, advancing both theory and practice in digital marketing ethics.

Disclosure statement

No potential conflict of interest was reported by the authors.

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Appendix

Interview Guide

Title: Exploring Consumer Perceptions of Authenticity and Emotional Impact in AI-Generated vs. Human-Generated Marketing Content

This study aims to explore how consumers perceive digital marketing content created by artificial intelligence (AI) tools (such as ChatGPT, Midjourney, and Sora) compared to content created by human marketers. We will explore your opinions about authenticity, trust, emotional reactions, and the role of AI in marketing. Your responses will remain anonymous and are used only for academic purposes. The interview will last approximately 45 to 60 minutes. You will be shown different examples of marketing content and asked to share your impressions.

Respondent Profile

Sex:

Age

Professional Activity:

Perceived Authenticity

Visuals: AI-generated marketing image vs authentic handmade brand image

Which image feels more authentic? Why?

………………………………………………………………………………………………………………………………………………………………………………………………

Credibility and Trust

Texts: AI-written blog post vs human testimonial

• Which text seems more credible to you? Why ?

………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………

• Does knowing a text is AI-generated affect your trust in the brand?

………………………………………………………………………………………………………………………………………………………………………………………………

Emotional Impact and Engagement

Videos: AI-generated ad vs Human video

• Which video affects you more emotionally? Why?

………………………………………………………………………………………………………………………………………………………………………………………………

Narrative Attachment and Credibility

Storytelling: AI co-written brand story vs authentic customer stories

• Which story feels more believable or engaging? Why?

………………………………………………………………………………………………………………………………………………………………………………………………

• Can AI-generated stories feel human to you?

………………………………………………………………………………………………………………………………………………………………………………………………

Ethical Reflection

• Should brands disclose when content is AI-generated?

………………………………………………………………………………………………………………………………………………………………………………………………

• Would it change your buying decision?

………………………………………………………………………………………………………………………………………………………………………………………………