Consumer Trust and Purchase Intention in AI-Generated Marketing Content Across Generational Cohorts

Introduction

The rapid adoption of generative artificial intelligence tools within marketing practice has fundamentally changed how brands produce advertising copy, product descriptions, social media content, and personalized promotional messaging, raising new questions about how consumers perceive and respond to content they know, or suspect, was generated by an algorithm rather than a human copywriter (Campbell et al., 2022). While AI-generated content offers marketers substantial efficiency gains, its effect on two outcomes central to marketing effectiveness, consumer trust and purchase intention, remains incompletely understood, particularly given growing evidence that consumers do not respond uniformly to AI-generated content and that generational cohort may significantly shape this response (Noble et al., 2022). This chapter introduces the background, problem, purpose, and design of a quantitative study examining the influence of AI-generated marketing content on consumer trust and purchase intention, and whether this influence differs meaningfully across generational cohorts.

Background Of The Study

Artificial intelligence has moved from a peripheral marketing tool to a central component of content production pipelines, with generative systems now capable of producing advertising copy, email campaigns, and social media posts at scale and with increasing sophistication (Huang & Rust, 2021). This shift has prompted a growing body of consumer research examining how awareness that content was AI-generated, as opposed to human-authored, shapes consumer evaluation of that content, with findings suggesting a complex and sometimes contradictory pattern of responses (Puntoni et al., 2021).

A substantial line of research has documented what has been termed algorithm aversion, a tendency for consumers to trust and prefer human-generated judgments and content over algorithmically generated equivalents, particularly in domains perceived as requiring subjective or emotionally nuanced judgment, a category into which persuasive marketing content plausibly falls (Longoni et al., 2021; Shin, 2021). At the same time, other research has identified conditions under which consumers exhibit algorithm appreciation rather than aversion, particularly when AI systems are perceived as more objective, consistent, or data-driven than human alternatives, suggesting that consumer response to AI-generated content is highly context-dependent rather than uniformly negative (Yalcin et al., 2022). Sundar’s machine heuristic framework offers one explanation for this variability, proposing that consumers apply a cognitive heuristic in which machine-generated content is automatically assumed to be more objective and less biased than human-generated content, a heuristic that can either enhance or undermine trust depending on the specific marketing context and consumer’s prior beliefs about AI (Sundar, 2020).

Generational cohort has emerged as a potentially important, though underexamined, factor shaping these responses. Generational cohort theory proposes that individuals who come of age during a shared historical and technological period develop relatively stable, shared attitudes and behavioral tendencies that persist across the life course, suggesting that cohorts differing in their formative exposure to digital and AI technologies may hold systematically different baseline attitudes toward AI-generated content (Twenge, 2023). Empirical research on disclosure of algorithmically generated or native advertising content has found generational differences in both recognition of and trust response to disclosed content, with younger cohorts generally demonstrating greater familiarity with, though not necessarily greater trust in, AI-generated material (Wojdynski & Golan, 2020; Zhang et al., 2023). However, direct empirical comparison of how AI-generated marketing content specifically affects trust and purchase intention across multiple generational cohorts within a single controlled study remains limited, with much of the existing literature examining either generational technology attitudes or AI-content trust in isolation rather than jointly (Kietzmann et al., 2020; Noble et al., 2022).

Statement Of The Problem

As marketing organizations increasingly adopt generative AI tools for content production, they face growing uncertainty regarding whether disclosed or detectable AI-generated content undermines the consumer trust and purchase intention that marketing content is designed to build, and whether this effect varies meaningfully across the generational cohorts that constitute a brand’s customer base (Campbell et al., 2022; Luo et al., 2021). While existing research has independently documented both algorithm aversion effects in consumer judgment and generational differences in technology attitudes, these two bodies of literature have not been sufficiently integrated to clarify whether generational cohort meaningfully moderates the trust and purchase-intention consequences of AI-generated marketing content specifically (Shin, 2021; Twenge, 2023).

This gap is problematic because marketing organizations currently lack clear, empirically grounded guidance regarding whether AI-generated content strategies should be differentiated by target generational cohort, and whether disclosure of AI involvement in content creation carries different reputational and conversion risks depending on the generational composition of a brand’s audience (Wojdynski & Golan, 2020). Without controlled empirical evidence directly comparing trust and purchase intention responses to AI-generated versus human-generated marketing content across cohorts, marketers risk applying a uniform content strategy that may be well-suited to some generational segments while inadvertently undermining trust and conversion among others (Noble et al., 2022; Yalcin et al., 2022).

Purpose Of The Study

The purpose of this quantitative, between-subjects experimental-survey study is to examine the influence of AI-generated marketing content, relative to human-generated marketing content, on consumer trust and purchase intention, and to determine whether this influence differs significantly across generational cohorts. Participants will be randomly assigned to view marketing content labeled as either AI-generated or human-generated, with underlying message content held constant across conditions, and will subsequently report their trust in the content and their purchase intention toward the advertised product.

Research Questions And Hypotheses

RQ1. Does disclosed content source (AI-generated versus human-generated) significantly influence consumer trust in marketing content?

H1. Consumers exposed to content disclosed as AI-generated will report significantly lower trust than consumers exposed to content disclosed as human-generated.

RQ2. Does disclosed content source significantly influence consumer purchase intention?

H2. Consumers exposed to content disclosed as AI-generated will report significantly lower purchase intention than consumers exposed to content disclosed as human-generated.

RQ3. Does generational cohort moderate the effect of disclosed content source on consumer trust and purchase intention?

H3. The effect of disclosed content source on trust and purchase intention will differ significantly across generational cohorts, with younger cohorts (Generation Z and Millennials) exhibiting a smaller trust and purchase-intention gap between AI-generated and human-generated content than older cohorts (Generation X and Baby Boomers).

Significance Of The Study

This study carries significance for consumer behavior theory, marketing practice, and generational technology research. Theoretically, the study extends the machine heuristic framework and algorithm aversion literature by directly testing whether generational cohort functions as a significant moderator of AI-content trust responses, integrating two previously largely separate research streams within a single controlled design (Sundar, 2020; Twenge, 2023).

Practically, findings may directly inform marketing content strategy, providing evidence-based guidance regarding whether AI-generated content disclosure should be differentiated, contextualized, or paired with specific trust-repair messaging depending on the generational composition of a target audience (Campbell et al., 2022). Given the accelerating adoption of generative AI tools across marketing departments of all sizes, evidence clarifying the conditions under which AI-generated content helps or harms consumer trust and conversion carries substantial commercial relevance (Huang & Rust, 2021). More broadly, this study contributes to ongoing public and regulatory discussions regarding AI content disclosure requirements in advertising, offering empirical evidence relevant to debates about whether, and for whom, AI disclosure meaningfully changes consumer response (Luo et al., 2021).

Theoretical Framework

This study is grounded in Sundar’s machine heuristic framework, which proposes that consumers apply a cognitive shortcut in which machine-generated content is automatically evaluated as more objective, mechanical, and potentially less trustworthy in emotionally persuasive contexts than human-generated content, a heuristic that shapes trust judgments independent of the actual quality of the content itself (Sundar, 2020). This framework provides the theoretical basis for hypothesizing that disclosed AI authorship will directly influence trust and purchase intention, independent of message content, which is held constant across experimental conditions in this study’s design.

This substantive framework is paired with generational cohort theory, which proposes that individuals who come of age during a shared sociotechnological period develop relatively stable, shared attitudes toward emerging technologies that persist across the life course and differentiate them from adjacent cohorts (Twenge, 2023). Integrating these two frameworks allows the study to test not only whether the machine heuristic operates as a general effect across consumers, but whether its strength is systematically moderated by generational cohort, reflecting differences in cohorts’ formative exposure to, and baseline familiarity with, AI-driven technologies (Zhang et al., 2023).

Nature Of The Study

This study will employ a quantitative, between-subjects factorial experimental-survey design, in which content source (AI-generated versus human-generated, disclosed via labeling) is manipulated while underlying marketing message content is held constant, and generational cohort is treated as a naturally occurring categorical moderating variable (Creswell & Creswell, 2023). Participants will be recruited through an online panel using quota sampling to ensure adequate representation across four generational cohorts: Generation Z, Millennials, Generation X, and Baby Boomers, classified according to established birth-year cohort boundaries.

Participants will be randomly assigned to view one of two versions of an identical marketing advertisement, differing only in a disclosure label identifying the content as either AI-generated or human-generated, after which they will complete validated measures of consumer trust and purchase intention. Data will be analyzed using two-way factorial ANOVA to test the main effects of content source and generational cohort, as well as their interaction effect, on both trust and purchase intention. Significant interaction effects will be followed by simple effects analysis to determine which specific generational cohorts exhibit statistically significant differences in response to AI-disclosed versus human-disclosed content.

Definitions Of Key Terms

Algorithm aversionThe tendency of individuals to trust and prefer human judgment or human-generated content over algorithmically generated equivalents, particularly in domains perceived as requiring subjective judgment (Longoni et al., 2021).

Consumer trustA consumer’s confidence in the honesty, reliability, and credibility of marketing content or the brand presenting it (Shin, 2021).

Generational cohortA group of individuals who were born within a similar time period and who share formative historical and technological experiences that shape relatively stable, common attitudes and behaviors (Twenge, 2023).

Machine heuristicA cognitive shortcut in which machine-generated content is automatically evaluated as more objective and mechanical, and potentially less trustworthy in emotionally persuasive contexts, than human-generated content (Sundar, 2020).

Purchase intentionA consumer’s self-reported likelihood of purchasing a product or service following exposure to marketing content (Puntoni et al., 2021).

AI-generated marketing contentAdvertising copy, product descriptions, or promotional messaging produced, in whole or in part, by a generative artificial intelligence system rather than a human author (Campbell et al., 2022).

Assumptions

This study operates under several assumptions. It is assumed that participants will attend to and accurately process the content-source disclosure label presented within each experimental condition, and that this disclosure will be perceived as credible rather than dismissed. It is assumed that participants’ self-reported generational cohort, based on birth year, accurately reflects the formative technological and sociocultural experiences theorized to shape generational attitudes toward AI (Twenge, 2023). It is further assumed that the validated trust and purchase intention instruments selected for this study possess adequate reliability and validity across all four generational cohorts sampled, and that holding message content constant across experimental conditions effectively isolates the influence of disclosed content source from confounding message-quality effects.

Scope And Delimitations

The scope of this study is delimited to consumer response to a single product category and a single marketing content format, presented as short-form advertising copy, and does not examine other content formats such as video, influencer-generated content, or long-form editorial content. The study is further delimited to four generational cohorts, Generation Z, Millennials, Generation X, and Baby Boomers, using established birth-year classification boundaries, and does not include participants from cohorts falling outside these ranges. The study is delimited to a single disclosure condition in which AI or human authorship is explicitly labeled, and does not examine undisclosed or ambiguously disclosed AI-generated content, nor does it examine longitudinal changes in trust following repeated exposure over time.

Limitations

Several limitations should be acknowledged. The use of a single product category and content format limits the generalizability of findings to other marketing contexts, product types, and content formats not examined in this study. Reliance on an online panel sample, while enabling quota-based generational representation, may not fully capture the diversity of consumer attitudes within each generational cohort, and panel participants may differ systematically from the broader consumer population in technology familiarity or survey-taking behavior. The experimental design’s reliance on explicit disclosure labeling does not reflect the full range of real-world marketing contexts, in which AI involvement in content creation is often undisclosed or only partially disclosed, potentially limiting the ecological validity of observed effects. Finally, the cross-sectional, single-exposure nature of this study cannot capture how trust and purchase intention responses to AI-generated content may evolve with repeated exposure or increasing marketplace familiarity with generative AI tools over time (Yalcin et al., 2022).

Summary

This chapter introduced the background, problem, purpose, and theoretical grounding of a proposed quantitative study examining the influence of AI-generated marketing content on consumer trust and purchase intention, and whether this influence is moderated by generational cohort. While existing research has independently documented both algorithm aversion effects and generational differences in technology attitudes, these two literatures remain insufficiently integrated with respect to AI-generated marketing content specifically, leaving marketers without clear, cohort-specific evidence to guide content strategy. Grounded in the machine heuristic framework and generational cohort theory, this quantitative, between-subjects experimental-survey study aims to clarify whether, and for which generational cohorts, disclosed AI authorship meaningfully undermines consumer trust and purchase intention, with implications for marketing practice, consumer behavior theory, and AI disclosure policy. Chapter Two will present a comprehensive review of the existing literature on AI-generated content, consumer trust, and generational technology attitudes, further situating this study within its scholarly context.

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