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Article

Artificial Intelligence Disclosure in Cause-Related Marketing: A Persuasion Knowledge Perspective

by
Xiaodong Qiu
1,2,
Ya Wang
1,*,
Yuruo Zeng
1 and
Rong Cong
1
1
School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China
2
Research Center for Central and Eastern Europe, Beijing Jiaotong University, Beijing 100044, China
*
Author to whom correspondence should be addressed.
J. Theor. Appl. Electron. Commer. Res. 2025, 20(3), 193; https://doi.org/10.3390/jtaer20030193
Submission received: 28 March 2025 / Revised: 4 July 2025 / Accepted: 21 July 2025 / Published: 2 August 2025
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Abstract

Integrating artificial intelligence (AI) and cause-related marketing has reshaped corporate social responsibility practices while triggering a conflict between technological instrumental rationality and moral value transmission. Building on the Persuasion Knowledge Model (PKM) and AI aversion literature, this research employs two experiments to reveal that AI disclosure exerts a unique inhibitory effect on consumers’ purchase intentions in cause-related marketing contexts compared to non-cause-related marketing scenarios. Further analysis uncovers a chain mediation pathway through consumer skepticism and advertisement attitudes, explaining the psychological mechanism underlying AI disclosure’s impact on purchase intentions. The study also identifies the moderating role of AI aversion within this chain model. The findings provide a new theoretical perspective for integrating AI disclosure, consumer psychological responses, and marketing effectiveness while exposing the “value-instrumentality” conflict inherent in AI applications for cause-related marketing. This research advances the evolution of the PKM in the digital era and offers practical insights for cause-related marketing enterprises to balance AI technology application with optimized disclosure strategies.

1. Introduction

As corporate social responsibility (CSR) becomes increasingly important, cause-related marketing (CRM) has gained popularity among businesses [1]. CRM is an innovative strategy that links business activities with social causes, and it is defined as a “profit-driven philanthropic act” [2]. This unique model of corporate giving is based on consumer purchases. Varadarajan and Menon describe CRM as “the process of formulating and implementing marketing activities that link a firm’s contributions to a designated social cause with consumer transactions of specific products or services, thereby achieving organizational and individual objectives” [3]. By engaging in CRM, companies not only meet their social responsibilities [4] and enhance their corporate image and reputation [5] but also improve consumer attitudes [6]. This creates a positive cycle of financial and social benefits. For consumers, buying products linked to CRM not only satisfies their material needs but also allows them to contribute to societal welfare through corporate donations. This behavior fulfills their altruistic motivations and reinforces their social values [7]. With the continuous rise in consumers’ expectations regarding corporate social responsibility [8,9], CRM has evolved from a mere marketing tool into a critical nexus bridging business value and social value.
While traditional CRM has established its foundational value in connecting commercial and social objectives, the rapid development of artificial intelligence (AI) is fundamentally reshaping how these strategies are conceived and implemented. This technological revolution presents both unprecedented opportunities and novel challenges for CRM practitioners and researchers alike. Artificial intelligence-generated content (AIGC) technology has advanced rapidly in the digital era, gaining widespread adoption in commercial settings due to its exceptional content-generation capabilities and user-friendly features [10,11]. An increasing number of enterprises now utilize AI to automate content creation, providing novel tools and frameworks for implementing CRM strategies. This integration enables tighter alignment between consumer purchasing intentions and CRM initiatives through intelligent means. The convergence of AI and CRM has emerged as a prominent marketing trend, enabling companies to create more targeted, personalized, and cost-effective campaigns. AI’s capacity for rapid content generation allows organizations to develop CRM advertisements at scale while maintaining consistency in messaging and brand alignment. This technological integration appears to offer a pathway for companies to amplify their social impact while optimizing operational efficiency.
However, this technological advancement introduces novel complexities that challenge conventional CRM dynamics and raise critical questions about consumer perceptions. The integration of AI into CRM contexts, while promising operational benefits, simultaneously creates unprecedented challenges that remain largely unexplored in existing literature. When AI is applied to CRM activities—particularly in CRM advertisement creation—a critical question arises: Does consumers’ awareness of AI’s involvement alter their responses to CRM campaigns? This question becomes particularly complex given CRM’s dual identity, where advertisements simultaneously convey social welfare messages and brand promotion objectives. While AI enables rapid content generation, consumers may fear that companies could exploit this technology to produce false or misleading materials promoting their charitable initiatives [12], thereby fostering skepticism toward CRM communications [13].
Existing studies have explored consumer reactions to AI when it serves as a predictive tool via data mining [14,15] but have largely overlooked the unique impacts of generative AI in CRM contexts [16]. Research on AI-generated advertisements primarily focuses on performance comparisons between AI- and human-created content [17,18], paying limited attention to differences in effectiveness within CRM-specific scenarios or to the impact of AI disclosure on consumers’ emotional and behavioral responses. Prior research demonstrates that consumers exhibit distinct attitudes and behaviors toward AI-generated content compared to human-created materials [19,20], correlating with cognitive biases, emotional responses, and AI aversion [21,22].
Amid global calls for the disclosure of AI-generated content, current legal frameworks establish general requirements for AI disclosure [23]. Yet, industry practitioners remain divided regarding whether and how to disclose AI involvement to consumers. Mixed findings—from negative consumer reactions [24] to neutral [25] or even positive outcomes [26]—highlight contextual dependencies. Although employing AI-generated advertisements in CRM may enhance operational efficiency in the advertising design and reduce production costs, it comes at the potential expense of reduced trust and less positive ad attitudes when disclosing the use of AI technology in ads [27]. However, the existing literature inadequately addresses consumers’ unique sensitivity to the coexistence of altruistic and commercial motives in CRM contexts, creating a critical knowledge gap that hinders both theoretical understanding and practical implementation of AI-driven CRM strategies.
These unresolved conflicts underscore the urgent need for a systematic investigation. Current literature inadequately addresses how AI disclosure interacts with CRM’s dual-identity nature or how consumers reconcile algorithmic involvement in socially conscious messaging. Furthermore, the mechanisms driving attitude shifts—particularly the mediating role of skepticism and the moderating influence of AI aversion—remain unexamined in CRM contexts. Consequently, this study proposes three research questions:
(1)
Does AI disclosure affect consumer purchase intention differently between CRM and non-CRM contexts?
(2)
How does disclosing AI involvement in CRM impact consumers’ purchase intentions?
(3)
What mechanistic role does AI aversion play in this process?
To address these questions, we propose a conceptual framework (see Figure 1) grounded in the PKM. The PKM offers a critical perspective for understanding consumer reactions to AI disclosure, positing that consumers scrutinize persuaders’ motives when persuasive intent is perceived [28]. In CRM contexts, AI disclosure may lead consumers to question the firm’s motives for using AI-generated content, activating persuasion knowledge [29]. AI aversion amplifies this skepticism by highlighting the incongruity of AI’s role in CRM. We empirically test this framework through two experiments. First, we compare consumer responses to AI-generated versus human-designed CRM advertisements and examine whether such differences persist in non-CRM contexts. Second, we analyze the mechanisms underlying the impact of AI disclosure on purchase intentions in CRM scenarios, focusing on the mediating roles of consumer skepticism and advertisement attitudes, as well as the moderating effect of AI aversion. Our findings aim to elucidate the implications of AI disclosure in CRM advertisements and provide actionable recommendations for firms to balance AI transparency with consumer engagement in CRM initiatives.

2. Theoretical Background and Research Hypotheses

2.1. Disclosure of Ad Creation Sources and Purchase Intention

In today’s digital era, advertisements serve as a critical bridge between firms and consumers [30]. The diversity of advertisement creation sources has become increasingly prominent, with AI-generated and human-designed advertisements emerging as two focal forms. However, scholarly opinions remain divided on their differential effects on consumer responses. Some researchers argue that AI-generated advertisements excel in standardization and scale production [31], matching human-designed content in quality [25,32], and fulfilling consumers’ functional needs [18]. Others contend that AI-generated ads may outperform human counterparts in certain aspects. For instance, their novelty and technological appeal can stimulate consumer curiosity, enhancing purchase intentions [33]. Additionally, the efficiency and cost-effectiveness of AI-generated ads make them particularly attractive for commercial applications [34]. Conversely, some studies highlight the unique strengths of human-designed advertisements, particularly in emotional depth and humanized expression. When addressing complex emotional or socially charged themes, human designers leverage delicate emotional depictions and ethical narratives to amplify the persuasive power of advertisements [24,35].
These research discrepancies suggest that the impact of advertisement creation sources on consumer responses may vary depending on marketing contexts or individual differences. CRM contexts involve marketing campaigns explicitly linked with prosocial causes (e.g., charity donations and environmental initiatives). These contexts heighten emotional involvement and moral salience, triggering consumers’ scrutiny of ad authenticity [24]. In contrast, non-CRM contexts focus on transactional exchanges (e.g., product features, promotions) where functional utility dominates consumer evaluations [36]. In CRM scenarios, where emotional depth and moral motives in advertisements become critical determinants of consumer responses [37,38], the discussion of ad creation sources (AI-generated vs. human-designed) gains heightened significance. CRM advertisements often address social welfare and ethical issues [39], activating consumers’ “moral identity” and prompting them to evaluate ad sources through an ethical lens. Consumers exhibit heightened sensitivity to these themes, scrutinizing the authenticity of ads and their emotional resonance [24]. While CRM participation evokes a “warm glow” [40], AI involvement risks diminishing this warmth [41]. Consumers expect CRM ads to reflect corporate sincerity, yet AI’s association with efficiency contradicts this expectation, fostering perceptions of inauthenticity [12]. Research indicates that CRM success largely hinges on advertisements’ ability to evoke emotional engagement and moral persuasion [42]. In CRM contexts, consumers demand higher standards for emotional depth, authenticity, and ethical intent in advertisements [43,44].
In contrast, non-CRM advertisements primarily emphasize utilitarian product or service attributes. When exposed to such ads, consumers prioritize functional information—including features, quality, and pricing [36]. Whether AI-generated or human-designed, advertisements that clearly and accurately convey this information are likely to capture consumer attention [45]. Consequently, the advertisement’s origin plays a negligible role in consumer decision-making within non-CRM contexts. This differential impact suggests that consumer reactions to ad-creation sources may be significantly amplified in CRM settings compared to non-CRM environments.
Based on this, we propose the following hypotheses:
H1: 
In CRM contexts, disclosing AI-generated advertisements (vs. human-designed advertisements) significantly reduces consumers’ purchase intentions.
H2: 
In non-CRM contexts, there is no significant difference in purchase intentions between disclosing human-designed and AI-generated advertisements.

2.2. Persuasion Knowledge Model and AI Disclosure

The Persuasion Knowledge Model (PKM) posits that consumers activate cognitive frameworks related to persuasive intent when encountering advertisements or marketing activities, aiming to discern the true objectives of such messages and assess their credibility [28]. The PKM highlights that consumers are not merely passive recipients of marketing messages; they actively evaluate such information by applying their understanding of persuasive motives and tactics (i.e., persuasion knowledge) to critically assess marketers’ intentions and strategies [46]. Specifically, when individuals receive information designed to influence them, the recognition of persuasive intent triggers the activation of persuasion knowledge, enabling them to analyze persuasive elements within the message and subsequently adjust their behavioral responses [22]. This process may lead to adverse outcomes, such as reduced purchase intentions [47]. When AI is disclosed as the creator of CRM advertisements, consumers must evaluate this new type of persuasive agent—artificial intelligence—using their existing knowledge frameworks. Acquiring persuasion knowledge often heightens consumer skepticism toward advertisements [48] and strengthens cognitive defenses [49,50].
The PKM provides a critical lens for understanding consumer responses to AI disclosure in CRM advertisements. AI disclosure informs consumers about the nature of an advertisement’s creation [22]. A core tenet of the PKM is that consumers actively evaluate persuasive agents’ motives and tactics when such intent is perceived [28]. From an information asymmetry perspective, the technical complexity of AI creates a significant knowledge gap between firms and consumers [51]. While firms possess comprehensive information about AI’s role in CRM campaigns—including data processing methods and decision-making protocols—consumers rely solely on limited disclosures. Due to this inherent information asymmetry, when firms disclose AI involvement in CRM activities, the PKM predicts a systematic cognitive process: consumers immediately make heuristic assessments of the company’s motives, which activates consumers’ persuasion knowledge [29].
The PKM suggests that when persuasion knowledge is activated, consumers develop skepticism toward the persuasive message, which, in turn, affects their attitudes and behavioral intentions. Studies demonstrate that disclosing AI’s role in advertisements negatively impacts purchase intentions [29]. Consumers tend to infer corporate motives for AI disclosure based on their understanding of the technology [52]. When consumers encounter AI disclosure in CRM advertisements, the PKM predicts they will question the authenticity and motives behind this choice. Compared to human-generated content, AI-generated content is often perceived as efficiency-driven and lacking genuine commitment to social causes [35]. Consumers may question the authenticity of AI-generated content [53], doubt whether it genuinely reflects the essence and values of philanthropic initiatives, or suspect that firms prioritize cost reduction and efficiency gains over content quality and authenticity [54]. Additionally, consumers might perceive AI disclosure as a marketing gimmick rather than a sincere commitment to social causes [55]. Such skepticism fosters resistance toward AI involvement in advertisements, particularly when consumers perceive AI-generated content as deceptive [56].
Some scholars argue that there is no significant difference in design quality between AI algorithms [32] and professional designers and even suggest that AI-generated designs may outperform human counterparts in stimulating consumer curiosity and enhancing purchase intentions [33]. AI disclosure can trigger consumer skepticism about the authenticity of AI-generated content. Such skepticism serves as a critical mediating mechanism, directly driving unfavorable attitudes toward advertisements [24,57], which, in turn, diminishes purchase intentions [49]. According to the PKM, consumer skepticism negatively influences consumers’ attitudes toward advertisements [58], thereby exerting a negative impact on their subsequent behavioral intentions. Thus, we posit that disclosing AI in CRM advertisements amplifies consumer skepticism, weakens advertisement attitudes, and reduces purchase intentions.
Based on this, we propose the following hypothesis:
H3: 
Consumer skepticism and advertisement attitude sequentially mediate the impact of AI disclosure on consumers’ purchase intentions.

2.3. The Moderating Role of AI Aversion

Artificial intelligence refers to “a growing interactive, autonomous, and self-learning resource that enables computational artifacts to perform tasks traditionally requiring human intelligence” [59]. In marketing, AI is widely applied to advertisement design, consumer insights, and personalized recommendations [10,11]. However, prior research reveals significant heterogeneity in consumer attitudes toward AI, encompassing positive and negative dimensions. Among these, AI aversion—a critical factor undermining consumer acceptance of AI-generated content [21,22]—reflects negative emotions and resistance toward AI applications [60,61]. This aversion typically stems from a limited understanding of AI and exaggerated perceptions of its risks [62,63], perceived absence of human-like interaction [35], and antipathy to AI’s substitution of human roles [64]. The inherent deceptiveness of AI-driven technologies, such as deepfakes that generate realistic yet fabricated content [12], exacerbates consumer aversion to AI. Additionally, AI is often perceived as lacking human-like qualities [24], leading consumers to doubt its capacity for empathy or emotional competence in handling nuanced tasks [65]. Such aversion significantly shapes how consumers process and respond to AI-related information [66].
The PKM suggests that consumers’ resistance to persuasion escalates when marketers employ tactics that conflict with their values [67]. In the context of CRM, disclosing AI as the source of advertisement creation introduces a value conflict for consumers with AI aversion. These individuals perceive AI as a persuasive agent, activating their persuasion knowledge [29]. AI aversion exacerbates the activation of persuasion knowledge [68]. For consumers with high AI aversion, AI disclosure represents exactly this type of value conflict. These individuals perceive AI as an inappropriate persuasive agent for social causes, amplifying the PKM-driven skepticism process [60]. Consequently, when firms disclose AI’s role in CRM advertisement creation, it inevitably elicits intense negative emotional reactions among these consumers.
High AI aversion exacerbates resistance toward AI’s involvement in CRM advertisements [24], driving consumers to avoid these ads during purchase decisions. The PKM predicts intensified skepticism when AI disclosure occurs in CRM contexts. These consumers view AI involvement as fundamentally incongruent with the altruistic nature of CRM, interpreting AI disclosure as “technological hypocrisy”—a tactic to reduce philanthropic costs or mask profit-driven motives [8,31]. They scrutinize the quality and reliability of AI-generated ads and question firms’ underlying motivations, perceiving such content as devoid of humanistic care and incapable of grasping the core ethos of philanthropic initiatives [60]. Some may even suspect firms of leveraging AI for commercial sensationalism rather than genuinely promoting public welfare [55]. This skepticism erodes trust in advertisements, worsens ad attitudes, and ultimately undermines CRM effectiveness [69].
In contrast, consumers with low AI aversion demonstrate weaker resistance to AI technology [70]. For these individuals, the PKM predicts attenuated skepticism because AI disclosure does not trigger the same level of value conflict. They perceive AI as non-violative of prosocial norms and demonstrate reduced skepticism even when AI disclosure occurs. These individuals may adopt a more receptive and inclusive attitude toward AI-generated advertisements.
Based on this, we propose the following hypotheses:
H4: 
AI aversion moderates the negative relationship between AI disclosure and consumer skepticism. This relationship is stronger when AI aversion is high (vs. low).
H5: 
AI aversion moderates the impact of AI disclosure on purchase intentions. The negative effect of AI disclosure on purchase intentions will be strengthened when AI aversion is high (vs. low).

3. Study 1

This experiment aimed to compare the effects of AI-generated versus human-designed advertisements on consumer responses in both CRM and non-CRM contexts (H1/H2).

3.1. Participants and Design

This study examined whether AI-generated and human-designed advertisements differentially influence consumers’ purchase intentions in CRM versus non-CRM scenarios, preliminary testing the potential risks of AI in CRM contexts (i.e., H1–H2). A 2 (ad created source: human-created vs. AI-created) × 2 (marketing context: CRM vs. non-CRM) between-subjects design was employed. Participants were recruited from Credamo (Credamo is a professional data collection platform widely used in psychology, consumer behavior, and organizational behavior research, with an online participant pool of over 3 million across all 34 provincial-level administrative regions in China, URL: https://www.credamo.com/, accessed on 7 February 2025), with a total of 240 individuals participating in the experiment. The sample size exceeded the minimum requirement of 128, as determined by G*Power 3.1 (effect size = 0.25, α = 0.05, power = 0.8). Each participant received a compensation of CNY 2 upon completing all survey items. After excluding 21 invalid responses (due to failed attention-check questions or uniform responses across all measures), the final sample comprised 219 participants (58% female). The mean age was 32.47 years (SD = 9.48), with 81.7% holding a bachelor’s degree or higher and a median monthly income of CNY 5000–10,000 (see Appendix A for details of sample characteristics).

3.2. Development of Stimulus Materials

To test the hypotheses, we designed experimental stimuli comprising print advertisements with integrated text and visuals. To exclude the influence of consumer brand familiarity on the experimental results, a fictitious brand named “Battleship Soccer” was utilized.
CRM Condition: To mitigate potential biases from cause-brand fit [42,71], the CRM group selected a charity initiative titled “Disaster Zone Reconstruction—Sports Education Dream Initiative.” The CRM advertisement included the slogan: “Spread love through soccer; build dreams for children in disaster zones. Sports education is pivotal to childhood development, and every soccer ball embodies hopes and aspirations. Support sports education in disaster-affected schools: Each purchase of Battleship Soccer contributes 0.02 yuan to the ‘Sports Education Dream Initiative”. Non-CRM Condition: The non-CRM advertisement excluded philanthropic appeals and used a neutral slogan: “Excellence on the field demands a superior ball—ignite passion, champion youth”. Both conditions provided identical product specifications (e.g., materials, dimensions) for “Battleship Soccer” to ensure experimental equivalence.
AI Disclosure Manipulation: All advertisements included a creator attribution label: “Created by AI” (AI-disclosure condition) or “Created by Humans” (human-disclosure condition) [24,29]. To address low baseline awareness of ad creator types [72], the label was displayed twice in each advertisement. Details of experimental materials are available in Appendix B Figure A1.

3.3. Procedure and Measures

All participants were randomly assigned to one of four experimental conditions: CRM context/AI-generated, CRM context/human-designed, non-CRM context/AI-generated, or non-CRM context/human-designed. Following the experimental instructions, participants were asked to imagine the following scenario: “Suppose you are planning to purchase a soccer ball and encounter this advertisement while browsing a shopping website.” After exposure to the stimulus material, participants indicated their purchase intentions and completed additional measures, including manipulation checks and demographic information (e.g., age, gender, education level).
Consumer purchase intention was measured using a three-item scale (Cronbach’s α = 0.845), including statements such as “I might consider purchasing the product in this advertisement,” “If needed, I would choose to buy the product in this advertisement,” and “I would select this product again in future purchases” [18]. All items were measured on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree).
For manipulation checks, this study verified the experimental manipulations of marketing context and ad creation source using two items: “I believe the above advertisement belongs to (1 = non-CRM context, 7 = CRM context)” and “I think the disclosed creator of the above advertisement is (1 = human, 7 = AI)” [72].

3.4. Results and Discussion

3.4.1. Manipulation Check

Analysis of variance (ANOVA) results confirmed the success of the manipulation test for advertisement creation sources and marketing contexts. Specifically, advertisement creation source: participants in the AI-disclosure condition reported significantly higher scores (MAI = 5.20, SDAI = 1.56) compared to the human-disclosure condition (Mhuman = 3.32, SDhuman = 1.50), F(1, 217) = 82.62, p < 0.001. Marketing context: Participants perceived non-CRM advertisements as belonging to non-CRM contexts (Mnon-CRM = 3.12, SDnon-CRM = 1.52), while CRM advertisements were recognized as CRM-related (MCRM = 5.12, SDCRM = 1.50), F(1, 217) = 95.93, p < 0.001.

3.4.2. Hypotheses Check

A 2 × 2 ANOVA was conducted with the disclosure of ad creation sources (AI vs. human) and marketing context (CRM vs. non-CRM) as independent variables and purchase intention as the dependent variable. The results of the ANOVA indicate that both ad generation source disclosure (F(1, 215) = 7.19, p = 0.008, η2 = 0.032) and marketing context (F(1, 215) = 8.99, p = 0.003, η2 = 0.04) had significant main effects on consumer purchase intention. Specifically, disclosing that AI-generated ads significantly reduced consumers’ purchase intention (Mhuman = 5.12, SDhuman = 0.84; MAI = 4.79, SDAI = 1.02). Furthermore, compared to the non-cause marketing context, the cause marketing context significantly increased consumers’ purchase intention (Mnon-CRM = 4.77, SDnon-CRM = 1.06; MCRM = 5.15, SDCRM = 0.76) (see Table 1).
The interaction effect between the disclosure of ad creation sources and marketing context was also significant (F(1, 215) = 5.09, p = 0.025, η2 = 0.023). A simple effects analysis (see Figure 2) revealed that in the cause marketing context, purchase intention for AI-generated ads (MAI = 4.84, SDAI = 0.90) was significantly lower than for human-created ads (Mhuman = 5.44, SDhuman = 0.48; F(1, 215) = 12.025, p = 0.001, η2 = 0.053). However, in the non-cause marketing context, there was no significant difference in purchase intention between AI-generated and human-created ads (F(1, 215) = 0.092, p = 0.762; MAI = 4.75, SDAI = 1.14; Mhuman = 4.80, SDhuman = 0.98) (see Table 2). We also included demographic variables such as gender, age, and education level as control variables, and the above effects remained robust. These findings confirm the hypotheses H1 and H2.
Study 1 compared the differences between AI-generated and human-designed advertisements in CRM and non-CRM contexts. The findings revealed that AI-designed CRM advertisements not only failed to enhance consumers’ purchase intentions but also exerted a detrimental effect. In Study 2, we focus on CRM contexts to investigate the mechanisms and boundary conditions underlying the impact of disclosing AI involvement in advertisement creation on purchase intentions, thereby further elucidating the nuanced effects of AI disclosure in CRM scenarios.

4. Study 2

Study 2 aimed to examine the impact of AI disclosure on consumer purchase intentions in CRM contexts and explore the underlying mechanisms and boundary conditions of this effect. We sought to validate the chain mediation role of consumer skepticism and advertisement attitudes and test whether AI aversion moderates the influence of AI disclosure on purchase intentions.

4.1. Participants and Design

A single-factor, three-level (AI disclosure: disclosing AI-generated, disclosing human-created, no disclosure) between-subjects design was employed. For this study, 240 participants were recruited from Credamo, exceeding the minimum sample size of 159 calculated by G*Power 3.1 (effect size = 0.25, α = 0.05, power = 0.8, groups = 3). Each participant received a compensation of CNY 2 after completing all survey items. After excluding 20 invalid responses (due to failed attention-check questions or uniform responses across measures), 220 valid responses remained (50.9% female). Participants had a mean age of 32.59 years (SD = 9.64), with 77.3% holding a bachelor’s degree or higher and a median monthly income of CNY 5000–10,000 (see Appendix A for details of sample characteristics).
Participants subsequently completed scales measuring consumer skepticism, advertisement attitudes, purchase intentions, and AI aversion.

4.2. Development of Stimulus Materials

To test the research hypotheses, we designed a stimulus scenario. The experimental stimulus material was bottled mineral water, and all advertisements were presented in a print ad format containing both text and images. To eliminate the potential influence of consumer brand familiarity on the experimental results, we selected a fictitious brand name, “Green Life,” for testing. This study selected the “Public Welfare Tree-Planting” initiative as the CRM context, using the slogan “Green Life Natural Mineral Water, sourced from deep glaciers, pure in every sip. For every bottle sold, Green Life donates 0.1 yuan to desert tree-planting” across all CRM advertisements. In the AI-disclosure group, a label stating, “This advertisement was generated by AI” was placed in the bottom-right corner of the ad; the human-disclosure group featured the label “This advertisement was created by humans,” while the no-disclosure group omitted any creator information. Detailed experimental materials are provided in Appendix B Figure A2.

4.3. Procedure and Measures

The procedure and measures were the same as in Study 1. Participants were randomly assigned to one of three experimental conditions: the AI disclosure group, the no-disclosure group, or the human disclosure group. All three groups viewed the same advertisement. In the disclosure conditions, participants were informed that the ad was either AI-generated or human-created, while the no-disclosure group received no such information. After viewing the stimulus, participants were asked to report their consumer skepticism, advertisement attitudes, AI aversion, purchase intention, and other measures, including attention checks, manipulation checks, and demographic information (such as age, gender, and education level).
Consumer suspicion was measured using a four-item scale adapted from Do et al. (2012) [73], with items such as “The disclosure of the ad’s image source makes me feel that the advertisement is deceptive”; “The disclosure of the ad’s image source makes me doubt the product quality”; “The disclosure of the ad’s image source makes me feel the advertisement contains misleading information and has an obvious persuasive intent”; and “The disclosure of the ad’s image source makes me suspicious of the ad’s marketing motives and feel that the ad is untrustworthy” (Cronbach’s α = 0.871). Attitude toward the advertisement was measured using three items adapted from Baek et al. (2024) and Song et al. (2024) [29,72]: “I would consider accepting this advertisement”; “I think this advertisement is good”; “My attitude toward this advertisement is positive” (Cronbach’s α = 0.904). AI aversion was measured using a four-item scale adapted from Grassini (2023) [70]: “I believe AI will not improve my quality of life”; “I believe AI will not enhance my work efficiency”; “I do not think I will use AI technology in the future”; “I believe AI technology has no positive significance for humanity” (Cronbach’s α = 0.919). Consumer purchase intention (Cronbach’s α = 0.844) was measured using the same items as in Study 1. All items were measured using a seven-point Likert scale (1 = strongly disagree, 7 = strongly agree).
For manipulation checks, we tested the effectiveness of the disclosure labels by asking participants, “Did the advertisement you just viewed mention the creator of its content?” (1 = Yes, explicitly stating it was created by a human; 2 = Yes, explicitly stating it was generated by AI; 3 = No, no creator information was mentioned; 4 = Not sure/did not notice).

4.4. Results and Discussion

4.4.1. Convergent and Divergent Validity of the Measurements

To assess the reliability, convergent validity, and discriminant validity of the constructs and their scales, a confirmatory factor analysis (CFA) was conducted. Results indicated good model fit (CMIN/DF = 1.12, RMSEA = 0.03, IFI = 0.993, TLI = 0.992, CFI = 0.993). All factor loadings exceeded 0.5 [74]. The average variance extracted (AVE) values surpassed the minimum threshold of 0.5, and composite reliability (CR) values were well above 0.7, collectively indicating good convergent validity and composite reliability across all dimensions [75] (see Table 3).

4.4.2. Manipulation Check

The validity of experimental conditions was verified using Fisher’s exact test. Participants accurately identified the three disclosure contexts—human disclosure, AI disclosure, and none disclosure—with correct identification rates of 93.3%, 91.8%, and 90.3%, respectively. Despite low expected cell frequencies in some categories, Monte Carlo-simulated Fisher’s exact test results (p < 0.001) confirmed the reliability of the experimental manipulations, indicating successful manipulation.

4.4.3. Hypotheses Check

Main Effects. A univariate analysis with purchase intention as the dependent variable and AI disclosure as the independent variable revealed significant differences across AI disclosure conditions (F(2, 217) = 43.167, p < 0.001, η2 = 0.285; see Figure 3). Consumers in the AI-disclosure group reported significantly lower purchase intentions (MAI = 4.11, SDAI = 1.1) compared to both the human-disclosure group (Mhuman = 5.20, SDhuman = 0.93) and the none-disclosure group (M = 5.12, SD = 0.64). No significant difference emerged between the human-disclosure and none-disclosure groups (p = 0.73; see Table 4). Thus, AI disclosure negatively impacts purchase intentions. These effects remained robust after controlling for demographic variables (gender, age, education), reconfirming H1.
Chain Mediation Analysis of Consumer Skepticism and Advertisement Attitudes. Based on the main effects, we conducted a mediation effect analysis focusing on the AI-disclosure and no-disclosure groups. Using Model 6 in SPSS 24.0’s PROCESS 4.1 plugin [76] with 5000 bootstrapping samples, we tested the chain mediation effect with AI disclosure as the independent variable, consumer skepticism and advertisement attitudes as sequential mediators, and purchase intention as the dependent variable. Results (see Table 5 and Table 6) showed that after introducing mediators, AI disclosure no longer had a significant direct effect on purchase intentions (b = −0.022, SE = 0.041, t = −0.529, 95% CI [−0.104, 0.060]). However, the sequential mediation pathway (AI disclosure → consumer skepticism → advertisement attitudes → purchase intentions) was significant (Ind3 effect = −0.1469, SE = 0.0475, 95% CI [−0.2426, −0.0559]), with specific path coefficients illustrated in Figure 4. These results remained consistent after controlling for demographics (gender, age, education), confirming H3.
Interaction Effect of AI Aversion and AI Disclosure. To mitigate multicollinearity, we mean-centered AI aversion and constructed an interaction term (AI disclosure × AI aversion). A moderated regression analysis was then conducted to examine the effects of AI aversion, AI disclosure, and their interaction on consumer skepticism. As shown in Table 7, the interaction term exerted a significant positive effect on consumer skepticism (b = 0.131, t = 2.905, 95% CI [0.042, 0.220]), indicating that AI aversion strengthens the impact of AI disclosure on skepticism. Thus, H4 was supported.
Moderated Chain Mediation Analysis. A moderated chain mediation analysis was conducted using PROCESS Model 83 with 5000 bootstrapping samples. AI disclosure served as the independent variable, consumer skepticism and advertisement attitudes as sequential mediators, AI aversion as the moderator, and purchase intention as the dependent variable. Results revealed a significant moderated chain mediation effect (effect = −0.647, SE = 0.032, 95% CI [−0.131, −0.005]). Specifically (see Table 8), under high AI aversion (+1 SD), the chain mediation effect of consumer skepticism and advertisement attitudes was significant (indirect effect = −0.135, SE = 0.044, 95% CI [−0.222, −0.052]), whereas under low AI aversion (−1 SD), the effect was nonsignificant (indirect effect = 0.002, SE = 0.04, 95% CI [−0.076, 0.085]). Path coefficients are detailed in Figure 5. These results remained robust after controlling for demographic variables (gender, age, and education level). Thus, H5 was supported.
Study 2 revealed the mechanisms and boundary conditions of AI disclosure’s impact on purchase intentions in CRM contexts. The findings demonstrate that consumer skepticism and advertisement attitudes sequentially mediate the effect of AI disclosure on purchase intentions. Furthermore, AI aversion negatively moderates this relationship, strengthening the detrimental influence of AI disclosure.

5. General Discussion

Through two sequential experiments, this study systematically investigated the impact and mechanisms of AI-generated advertisement disclosure on consumer behavioral intentions in CRM contexts. Study 1 employed a 2 × 2 between-subjects design. By comparing CRM and non-CRM scenarios, it revealed that while consumers exhibited higher overall purchase intentions in CRM contexts, the negative effect of AI disclosure was significant only in CRM contexts, highlighting the highly context-dependent nature of AI disclosure effects. Study 2 focused on CRM contexts using a single-factor, three-level design. It further validated the unique inhibitory effect of AI disclosure on consumer behavioral intentions in CRM scenarios, uncovering the chain mediation role of consumer skepticism and advertisement attitudes. Additionally, Study 2 proposed a dual-stage moderation model of AI aversion: AI aversion strengthens AI disclosure’s triggering of consumer skepticism and intensifies the negative transmission from skepticism to behavioral intentions via cognitive-affective pathways. Collectively, the findings demonstrate that when marketing activities involve moral value transmission, the instrumental rationality of AI technology conflicts with the ethical attributes of CRM, activating consumers’ persuasion knowledge defense mechanisms.

5.1. Theoretical Contributions

First, this study extends the existing literature on CRM. Current CRM research predominantly focuses on analyzing CRM campaigns themselves, such as the impact of consumers’ familiarity with charitable initiatives [77], donation amounts [78], and cause-brand fit [79,80] on CRM effectiveness. Few studies have explored the role of AI technology in CRM contexts. By integrating the technical attributes of generative AI into the activation framework of the PKM, our findings reveal a cognitive process distinct from traditional PKM research centered on advertiser intent inference [12]: AI disclosure in CRM triggers consumer skepticism, which negatively influences advertisement attitudes and subsequently suppresses purchase intentions. Crucially, we emphasize that AI disclosure in morally charged CRM advertisements activates consumers’ “moral hypocrisy attribution,” intensifying skepticism toward CRM campaigns and deepening the theoretical explanatory power of the PKM in CRM contexts.
Second, our research significantly contributes to the literature on consumer responses to AIGC. While technological advancements have expanded AIGC’s marketing applications [19], studies examining consumer attitudes toward AI-generated advertisements remain limited. Although Gu et al. (2024) [17] investigated consumer acceptance of AI-generated ads from a content effectiveness perspective, they overlooked AI’s technological instrumentality. By examining AI disclosure in high-morality CRM contexts, this study uncovers the negative impact of AI’s instrumental attributes on purchase intentions. The finding contrasts with prior work suggesting AI disclosure either does not affect brand attitudes [25] or enhances consumer engagement through increased transparency [81]. Responding to Baek et al.’s (2024) [29] call, we compare AI disclosure effects across CRM and non-CRM contexts, revealing consumers’ unique sensitivity to AI involvement in CRM. This study offers fresh insights into how consumers process AI-disclosed advertisements, highlighting the context-dependent and complex nature of AI disclosure effects.
Finally, grounded in consumers’ skepticism toward AI technology [82], we find that AI disclosure labels negatively affect consumer responses to AI-generated advertisements. This conclusion aligns with Arango et al. (2023) [24], who observed reduced donation intentions for AI-generated charity ads. However, our research extends this by incorporating the role of consumers’ subjective attitudes toward AI [83] and empirically validating the moderating effect of AI aversion. We demonstrate that high AI aversion not only directly intensifies the initial processing of disclosed information—thereby strengthening consumer skepticism—but also indirectly exacerbates resistance to the purchase intention of AI-generated CRM advertisements through cognitive-affective transmission pathways. These findings suggest that the negative effects of AI disclosure seem to be driven by humans’ subjective attitudes toward AI, offering novel theoretical insights into understanding the mechanisms underlying the negative impact of AI disclosure and expanding our understanding of consumer responses to AI-involved advertisement creation in CRM contexts.

5.2. Managerial Implications

Beyond theoretical contributions, this study offers practical insights for marketing practitioners, particularly firms and advertisers employing AIGC in CRM. While AIGC provides cost reduction and design efficiency benefits [34], firms must balance these advantages against potential negative consumer responses [24]. Advertisers should strategically align AIGC’s efficiency gains with advertising effectiveness, adapting disclosure practices to avoid over-transparency that triggers adverse reactions.
This study found that disclosing AI’s involvement in CRM advertisement creation significantly reduces consumers’ purchase intentions. Given the increasing legal and regulatory requirements for AI-generated content disclosure [23,25], firms and advertisers need to find a balance between marketing effectiveness and transparency. First, strengthening consumer education on AI through awareness campaigns that explain the role of AI in CRM can help mitigate consumer aversion to AI. Second, given the significant disparity in consumer purchase intentions between disclosing human involvement versus AI involvement, it is advisable to adopt a compromise approach by gradually disclosing AI’s role (e.g., initially emphasizing human participation or selectively disclosing AI’s use in non-critical design elements) [26,27,54]. This phased disclosure may allow consumers to adapt to AI’s evolving role, thereby reducing the negative impact of AI disclosure on purchase intentions. Additionally, firms can refine consumer profiles based on AI aversion levels and develop personalized disclosure strategies to alleviate negative impacts and improve advertisement acceptance and effectiveness.
Furthermore, these findings provide implications for policymakers and legislators. Mandatory disclosure of AIGC in advertisements may negatively affect performance. Our research demonstrates that in CRM contexts, disclosing AI-generated ads heightens consumer skepticism, diminishing ad appeal and efficacy. Thus, policymakers should incorporate this potential adverse effect into regulatory design, advocating for flexible disclosure frameworks to avoid overregulation and maintain an equilibrium between transparency and advertising effectiveness. Especially in CRM scenarios, we recommend establishing tiered disclosure systems and exemption clauses based on advertisements’ philanthropic attributes to ensure both regulatory transparency and the practical impact of CRM campaigns.

5.3. Limitations and Future Directions

While this study contributes to theory and managerial practice, several limitations warrant attention and offer avenues for future research. First, the current research focused solely on text-image advertisements for CRM, though CRM campaigns employ diverse formats (e.g., video, audio, and interactive ads) [84]. Future research could employ eye-tracking experiments to observe consumers’ visual attention toward AI-generated content or utilize electroencephalography (EEG) to capture users’ neural responses during consumer–AI content interactions. Such investigations would systematically compare how AI involvement affects consumer responses across these formats, thereby contrasting AI’s impact in CRM campaigns under different advertising modalities. Second, while we examined the presence or absence of AI disclosure, we did not distinguish whether advertisements were solely AI-generated, AI–human collaborative creations, or exclusively human-generated [19]. Future studies could examine the degree of human–AI collaboration in CRM, specifically investigating how disclosing varying degrees of AI involvement in CRM advertisements (e.g., “AI generation + human review” or “human-led + AI optimization”) influences consumer behavior. This will help to deepen both theoretical and practical insights. Finally, this study employed scenario-based experimental methods with simulated advertisements, capturing only consumers’ immediate reactions. The long-term effects of AI disclosure on behavior remain unexplored. Future research could collaborate with firms engaged in CRM campaigns to collect longitudinal tracking data in real-world settings, observe how AI disclosure in genuine good cause marketing scenarios affects consumer purchasing behavior, and analyze whether consumer responses to AI disclosure evolve with technological advancements or information dissemination over extended periods.

Author Contributions

Conceptualization, X.Q. and Y.W.; data curation, Y.W., Y.Z. and R.C.; investigation, X.Q., Y.Z. and R.C.; methodology, X.Q. and Y.W.; supervision, X.Q. and R.C.; validation, Y.W.; funding acquisition, X.Q.; visualization, Y.Z. and R.C.; writing—original draft, Y.W.; writing—review and editing, X.Q., Y.W. and Y.Z. All authors have read and agreed to the published version of the manuscript.

Funding

Key Project of the National Social Science Foundation of China: 15AGL002; National Natural Science Foundation of China: 70302001.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Business Administration Department, School of Economics and Management, Beijing Jiaotong University (No. 20240915, 15 September 2024).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Study 1Study 2
CharacteristicGroupNumberPercentageNumberPercentage
GenderMale9242%10849%
Female12758%11251%
Age<1811%42%
18–3010448%9744%
31–406932%7635%
41–503516%3114%
>50105%126%
JobStudent5023%4822%
Company employee9945%9041%
Government/institution personnel3215%4018%
Freelancer2210%2411%
Others167%188%
Highest educationPh.D.146%146%
Master’s degree6831%5626%
Bachelor’s degree9744%10046%
College and below4018%5023%
Income<20002612%199%
2000–50005425%6831%
5001–10,0007333%8438%
>10,0006630%4922%

Appendix B

Jtaer 20 00193 g0a1
Figure A1. Study 1: Stimuli.
Figure A1. Study 1: Stimuli.
Jtaer 20 00193 g0a1
Jtaer 20 00193 g0a2
Figure A2. Study 2: Stimuli.
Figure A2. Study 2: Stimuli.
Jtaer 20 00193 g0a2
Notes: The original ads were presented in Mandarin, and the above ads are the translated versions.

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Figure 1. Conceptual model.
Figure 1. Conceptual model.
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Figure 2. The influence of AI disclosure and marketing type on purchase intention.
Figure 2. The influence of AI disclosure and marketing type on purchase intention.
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Figure 3. The impact of AI disclosure on consumers’ purchase intention.
Figure 3. The impact of AI disclosure on consumers’ purchase intention.
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Figure 4. Bootstrapping mediation analysis.
Figure 4. Bootstrapping mediation analysis.
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Figure 5. Bootstrapping mediator analysis with regulation.
Figure 5. Bootstrapping mediator analysis with regulation.
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Table 1. Pairwise comparisons (main effect).
Table 1. Pairwise comparisons (main effect).
IJMean Difference (I–J)Std. ErrorSig.95% Confidence Interval for Difference
Lower BoundUpper Bound
Marketing context01−0.368 *0.1230.003−0.61−0.126
AI disclosure010.329 *0.1230.0080.0870.571
Note. * p < 0.05. Based on estimated marginal means. Marketing context: 0 = non-CRM,1 = CRM. AI disclosure: 0 = human, 1 = AI.
Table 2. Pairwise comparisons (interaction).
Table 2. Pairwise comparisons (interaction).
Marketing Context(I)AI Disclosure(J)AI DisclosureMean Difference (I–J)Std. ErrorSig.95% Confidence Interval for Difference
Lower BoundUpper Bound
0010.0520.1720.762−0.2880.392
10−0.0520.1720.762−0.3920.288
1010.606 *0.1750.0010.2620.95
10−0.606 *0.1750.001−0.95−0.262
Note. * p < 0.05. Based on estimated marginal means. Marketing context: 0 = non-CRM, 1 = CRM. AI disclosure: 0 = human, 1 = AI.
Table 3. Reliability and validity analysis.
Table 3. Reliability and validity analysis.
VariablesItemsEstimateAVECRCronbach’s α
AIAAIA10.8170.74090.91950.919
AIA20.894
AIA30.881
AIA40.849
CSCS10.750.6260.86970.871
CS20.818
CS30.846
CS40.746
AdAAdA10.8690.75870.90410.904
AdA20.866
AdA30.878
PurPur10.8040.64270.84360.844
Pur20.803
Pur30.798
Note: AIA = AI aversion, CS = consumer skepticism, AdA = advertisement attitude, Pur = purchase intention. The same below.
Table 4. Multiple comparisons.
Table 4. Multiple comparisons.
(I) (J) Mean Difference (I–J)Std. ErrorSig.95% Confidence Interval
Lower BoundUpper Bound
Tamhane−10−0.080.090.73−0.300.13
11.01 *0.150.000.641.38
0−10.080.090.73−0.130.30
11.09 *0.140.000.751.44
1−1−1.01 *0.150.00−1.38−0.64
0−1.09 *0.140.00−1.44−0.75
Note. * p < 0.05. Based on observed means. −1 = none disclosure, 0 = human disclosure, 1 = AI disclosure.
Table 5. Regression results of the chain mediating effects model (N = 145).
Table 5. Regression results of the chain mediating effects model (N = 145).
Outcome VariablePredictive VariableR2FbSEstLLCIULCI
CSAI disclosure0.075 11.663 0.2970.087 3.415 0.125 0.469
AdAAI disclosure0.779 250.545 −0.450 0.052 −8.652 −0.553 −0.348
CS −0.840 0.048 −17.476 −0.935 −0.745
PurAI disclosure0.867 305.868 −0.022 0.041 −0.529 −0.104 0.060
CS −0.234 0.055 −4.264 −0.342 −0.126
AdA 0.588 0.054 10.896 0.481 0.694
PurAI disclosure0.231 42.917 −0.503 0.077 −6.551 −0.655 −0.351
Note: All estimated coefficients are unstandardized. LLCI, lower limit of the 95% CI, and ULIC, upper limit of the 95% CI; and number of bootstrap samples for percentile bootstrap confidence intervals is 5000.
Table 6. Result and comparison of chain mediating effect (N = 145).
Table 6. Result and comparison of chain mediating effect (N = 145).
EffectBoot SEBoot LLCIBoot ULCI
Total effect−0.50310.0768−0.6549−0.3513
Direct effect−0.02190.0827−0.10360.059
Total indirect effect−0.48120.0899−0.6527−0.3028
Ind1−0.06960.0258−0.1275−0.0262
Ind2−0.26480.0533−0.3703−0.1671
Ind3−0.14690.0475−0.2426−0.0559
Note: Ind1 is the mediation effect of AI disclosure → CS → Pur, Ind2 is the mediation effect of AI disclosure → AdA → Pur, Ind3 is the mediation effect of AI disclosure → CS → AdA → Pur. Boot SE, Boot LLCI and Boot ULCI are estimated SE under bias-corrected percentile bootstrap method, and 95% CI lower and 95% CI upper, and Boot LLCI and Boot ULC do not overlap with zero.
Table 7. Result of moderation effect test.
Table 7. Result of moderation effect test.
Outcome VariablePredictive VariableUnstd.SEstp95% Confidence Interval
LLCIULCI
Consumer SkepticismAI disclosure0.1350.0472.8800.0050.0420.227
AI aversion0.8680.04519.2420.0000.7790.958
Int_10.1310.0452.9050.0040.0420.220
R2 = 0.745, F = 137.315, p < 0.001, Int_1 = AI disclosure × AI aversion.
Table 8. Results of the moderated chain mediation analysis.
Table 8. Results of the moderated chain mediation analysis.
ModeratorPath: AI Disclosure → CS → AdA → Pur
EffectBoot LLCIBoot ULCI
High AI aversion (+1 SD)−0.1347−0.2218−0.0522
Low AI aversion (−1 SD)0.0016−0.07540.0849
Didd (high–low)−0.1363−0.1382−0.047
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MDPI and ACS Style

Qiu, X.; Wang, Y.; Zeng, Y.; Cong, R. Artificial Intelligence Disclosure in Cause-Related Marketing: A Persuasion Knowledge Perspective. J. Theor. Appl. Electron. Commer. Res. 2025, 20, 193. https://doi.org/10.3390/jtaer20030193

AMA Style

Qiu X, Wang Y, Zeng Y, Cong R. Artificial Intelligence Disclosure in Cause-Related Marketing: A Persuasion Knowledge Perspective. Journal of Theoretical and Applied Electronic Commerce Research. 2025; 20(3):193. https://doi.org/10.3390/jtaer20030193

Chicago/Turabian Style

Qiu, Xiaodong, Ya Wang, Yuruo Zeng, and Rong Cong. 2025. "Artificial Intelligence Disclosure in Cause-Related Marketing: A Persuasion Knowledge Perspective" Journal of Theoretical and Applied Electronic Commerce Research 20, no. 3: 193. https://doi.org/10.3390/jtaer20030193

APA Style

Qiu, X., Wang, Y., Zeng, Y., & Cong, R. (2025). Artificial Intelligence Disclosure in Cause-Related Marketing: A Persuasion Knowledge Perspective. Journal of Theoretical and Applied Electronic Commerce Research, 20(3), 193. https://doi.org/10.3390/jtaer20030193

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