Beyond appearances: The limits of surface anthropomorphization in countering AI resistance

The practice of anthropomorphizing, or humanizing, Artificial Intelligence (AI) technologies to improve AI evaluation and purchase intentions has become standard practice in many industries [1–3]. Companies have widely adopted anthropomorphism practices by giving AI technologies a human name, voice, and appearance [3–5]. For example, many smart speakers (e.g., Apple, Google, and Amazon’s smart speakers) are equipped with natural human voice and speech mannerism [1,6]. Samsung’s Neon features lifelike artificial humans designed to serve as virtual receptionists, customer service agents, news reporters, and more [7]. Many AI technologies are also given human names, including Amazon’s Alexa and Samsung’s Sam [3,8,9]. Over the last decade, these humanization efforts have improved AI acceptance among consumers [8]. This positive effect has also been documented by numerous empirical studies highlighting the benefits of AI anthropomorphism efforts on AI acceptance and purchase intention [8–10].

Nevertheless, there are limits to the benefits of anthropomorphism [11]. Despite the general increase in AI acceptance attributed to anthropomorphism efforts, there remains a considerable level of consumer resistance to AI technologies [11,12]. People have previously been documented to exhibit aversion to AI technologies [13,14], including those of anthropomorphized ones [11]. For example, Longoni et al. [14] found that consumers tend to resist medical AI, perceiving it as less capable of understanding individual patient needs. Similarly, Castello et al. [13] showed that consumers are more resistant to AI technologies in subjective domains (e.g., creative or emotional tasks) than in objective domains (e.g., financial advice generation). Further, those who have received assistance from AI technologies (e.g., AI-generated service support, medical advice) would often seek a second opinion from human representatives [15,16]. The degree of AI resistance does vary across contexts. For example, people have been documented to be more resistant to AI tools in hedonic (e.g., indulgent products, sensory experiences) than utilitarian (e.g., health benefits, functionality) domains [17]. People also resist AI solutions more when they are used to make decisions with high stakes (e.g., determining who receives vaccines for a deadly flu variant) compared to those with low stakes (e.g., determining who receives vaccines for a mild flu variant) [18]. Although contextual variation exists, consumer resistance appears to be a consistent issue across domains, underscoring the need to address its underlying drivers.

There are several key reasons for AI resistance, including AI technologies’ perceived opacity, lack of emotions, and inflexibility [18]. First, people perceive AI systems as too opaque, with a “black box” decision logic that is hard to understand [19]. In turn, this leads to mistrust, particularly when outcomes are unexpected or explanations are unclear [19]. Second, AI technologies are often seen as lacking emotional capability [18,20], making them less suitable for tasks perceived as subjective [13]. Third, many believe that AI systems are inflexible, rigid, and lack the adaptability that humans possess [21]. This widespread belief likely arises “...because, historically, machines have operated based on simpler, non-adaptive algorithms that performed only narrow tasks” [18]. This is supported by Longoni et al. [14], who found that a key reason for AI resistance is the perception that AI technologies lack the cognitive flexibility to account for individual uniqueness. Taken together, these factors help explain why consumers continue to resist AI technologies. Overall, while the specific reasons for this residual resistance are widely varied, one common thread among them is the perception that AI technologies lack the inherent qualities that humans have and therefore cannot fully replace humans.

AI resistance persists even when AI technologies are designed to look and sound human [11]. This highlights a gap between perceived appearance and essence, and reveals the limits of current anthropomorphism efforts. This also suggests a deeper theoretical gap: although AI technologies can be designed to appear human, they are still widely viewed as lacking authentic human qualities. This contributes to ongoing AI resistance across domains, marking a notable challenge in need of resolution.

Human at the core: Human training Data (HTD) as a pathway to reduce AI resistance

Despite advances in humanlike design, AI technologies are still seen to lack authentic human qualities, contributing to ongoing AI resistance. What remains overlooked is that many AI models are built upon data that originates from humans. This form of training data embeds human patterns of thought, judgment, and behavior directly into AI systems through their learning processes. In this way, AI does not merely mimic humans in form. Rather, it also inherits distinctly human qualities from the data on which it is trained. Yet, consumers rarely recognize these deeply embedded human qualities, focusing instead on the mechanical nature of AI technologies.

In this research, we propose that making the human origins of AI training data explicit may help bridge this perceptual gap and reduce resistance to AI technologies. In this regard, we introduce the concept of human training data (HTD). Humans make a major component of AI training data through extensive datapoints generated by their interactions and behaviors. For example, voice assistant AIs are often trained on human speech in the development of their natural language processing capability, allowing them to exhibit humanlike speech and language capabilities [22]. The medical field has observed the use of AI consultation systems trained on physician judgment data to generate advice to patients [23]. Further, some self-driving car AIs are also trained on human driving behavior, which may result in adherence to social norms and even rule-breaking behavior [24]. These examples show that AI systems are often trained on data that reflects how humans think, feel, and behave. We refer to these as human training data (HTD), which is a training dataset that is derived from the cognitive, affective, and behavioral human processes captured within the dataset.

Importantly, HTD originates from humans themselves. As such, AI systems trained on HTD will absorb the human processes embedded within the data and exhibit these through their outputs. This renders AI more human, not by mimicking surface traits or superficial human appearances, but by internalizing the deep psychological processes behind human behavior. Through this, AI systems trained on HTD would inherit the human qualities they are perceived to lack at their core. For example, an HTD-trained medical AI system may inherit the cognitive flexibility of real medical professionals, who often have to take the unique characteristics of each patient into account when forming a diagnosis [23]. As another example, an HTD-trained customer service AI system may possess the emotional intelligence of human service representatives, allowing AI service reps to express empathy to frustrated customers [22].

Yet, there is limited public awareness of the common use of HTD in AI systems. Consumers continue to perceive AI technologies as foreign and lacking human qualities [12]. This lack of awareness may be attributed to the general lack of explainability and transparency regarding the inner workings of AI systems [19,25]. Presently, there is limited explainability on an AI technology’s inner workings, how it operates, and how it was trained [19,25]. This may be traced back to the insufficient communication from technology companies regarding how their AI systems are trained [19,25]. In turn, this has been documented to contribute to the issue of consumer resistance towards AI technologies [25–27]. Ultimately, the disconnect between an AI system’s inner workings and the extent of people’s understanding of it creates a consequential gap that contributes to persistent resistance to AI technologies.

The divide between the actual inner workings of AI systems and the extent to which they are understood by the public presents an intriguing research opportunity. The limitations of surface anthropomorphism strategies that revolve around superficial human qualities (e.g., giving AI technologies a human name, voice, and appearance) call for alternative anthropomorphism strategies that possess the capability to produce perceived humanization that reaches the core of AI technologies, allowing people to reevaluate their pre-existing mental model regarding AI technologies’ lack of inherent human qualities. For that, we propose the provision of HTD explanation to bring these human qualities to light and ultimately shift people’s mental model on these technologies and address the overarching problem of this research.

The present research

This paper puts forward HTD explanation as a source-based explainable AI (XAI) solution for AI resistance. We address the research question “How does HTD explanation influence consumer purchase intention?” To this, we propose HTD explanation to bring human presence within an AI system’s training dataset to the forefront. Consequently, this highlights the presence of humans at the core of said system. We expect this to result in the perception of symbolic contact between humans and AI technologies, where awareness of human inputs may prompt people to create a meaningful psychological link between humans and AIs. In accordance with the contagion effect, we anticipate this symbolic contact to trigger the process of perceived essence transfer (PET) from human sources to the AI recipient. This process will evoke the perception of AI technologies embodying the essence and immaterial qualities of humans that are captured within the dataset. In turn, we predict this to shift people’s mental model of AI technologies from that of cold unfeeling machines to entities that embody human qualities. We expect this to produce more positive perceptions of AI technologies, leading to higher purchase intention and ultimately reduced AI resistance.

This research makes three key contributions, each with its own nuance. First, we introduce HTD explanation as a source-based XAI strategy and extend explainability research by examining how making the human origins of AI visible can shape consumer perceptions. Second, we offer initial evidence that PET may serve as a promising explanatory mechanism for how consumers make sense of the human qualities embedded in AI technologies. In doing do, our research explores the concept of deep humanization, a perspective on how consumers may perceive AI technologies as fundamentally human-like, not merely through surface-level cues (e.g., voice or appearance), but through perceived exposure to human cognitive, emotional, and behavioral data during development. Third, our moderation findings offer preliminary insight into the heterogeneous effects of HTD explanation. Our findings indicate that HTD explanation may backfire when presented to individuals with high pre-existing trust in AI systems, leading to a decrease in their adoption intention. This underscores the importance of considering individual-level factors in the design and deployment of AI communication strategies.

Human training data (HTD)

Training data refers to the data that is used to construct an AI system, and serves as a core input to AI technologies [28]. It serves as the primary source of information to make predictions, decisions, and other forms of output [29,30]. It also lays the foundation for the intelligence embedded in AI systems, shaping their ability to understand, learn, and adapt to various tasks and challenges.

As previously noted, humans form a major part of AI training datasets. Such datasets act as foundational material upon which AI systems are trained to understand and replicate the thought processes, decisions, and behavioral patterns of humans. We refer to such data as HTD. HTD is routinely used in AI systems across various contexts, including retail, consulting services, and transportation [22,24,31]. To synthesize the scope of HTD applications, Table 1 outlines key uses of HTD across different AI systems.

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Table 1. HTD applications in AI systems.

https://doi.org/10.1371/journal.pone.0339482.t001

Table 1 summarizes HTD applications across different domains and AI systems. While we focus on HTD as a general feature of AI systems, we recognize that human training data may take on different forms. To this, we put forward three different types of human training data: cognitive, affective, and behavioral. We propose this definition and typology based on the tripartite model commonly used in psychology and neuroscience (e.g., [37,38]). This classification is often used to break down complex human processes into distinct categories for better understanding and has similarly influenced the marketing field. In consideration of the human origins of the training data under focus, we opted to apply the tripartite model for classifying different types of human training data.

We define cognitive human training data as data that is derived from human thinking processes. Cognition refers to the mental activity of information processing [39]. It encompasses various processes including memory, pattern identification, perception, problem solving, and mental imagery [40]. In line with this literature definition, we propose cognitive human training data to encompass data on human judgments, decision logic, problem solving process, and information processing. In accordance with the psychology literature on cognition [39,40], we propose that cognitive human training data would contain information on how humans make decisions, interpret situations, weigh risks, and rank their priorities. The integration of human decision logic and mental models within a given cognitive human data means that humans and AI systems will share decision logic of the same nature. Koren et al. [23] offer an illustrative example of cognitive HTD, where a medical AI consultant is trained on data containing rich information on the judgment, decision logic, and problem-solving processes used by medical professionals.

We define affective human training data as data that is derived from human affect and emotions. Affect is defined as the experience of feelings and emotions [41]. Emotion is defined as a strong affective reaction tied to a specific cause, including a specific event, object, person, or experience [42]. This includes both positive and negative emotional states ranging from happiness, excitement, sadness, anger, fear, and more [41]. In line with this, we define affective human training data as data that is derived from human affect and emotions, including both positive and negative emotional states. Many customer service bots that are trained using affective data are equipped with sentiment analysis capabilities, and possess the ability to identify customer emotions and respond appropriately to these emotional cues [43]. In turn, this informs AI technologies of what emotions to display in response to certain situations [44], while data on tones of voice have been used to inform AI technologies on what tone to adopt to convey certain emotions [45].

Finally, we define behavioral HTD as data that is derived from human actions and behavior. Behavior is defined as tangible physical actions that are observable to others [46]. This includes data on physical movements and actions that humans make when interacting with their surroundings [46], e.g., walking [47], hand and legs movements when interacting with objects and surrounding environment [34,48,49] Behavioral training data serve as a bridge between AI systems and human actions. Behavioral training data provides AI systems with information and understanding into the nuanced ways in which humans interact with their surroundings. This allows AI systems to mimic human behavior and produce behaviors that are akin to human behavior. The insights derived from this training dataset also provides AI systems with information on how they need to respond to certain events in a manner that is appropriate to the human audience.

These different HTD types provide AI systems with an understanding of the nuanced ways in which humans interact with their surroundings, and may be used in training AI systems on how to behave in a humanlike manner. We now summarize these three distinct types of HTD in Table 2.

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Table 2. Human training data types.

https://doi.org/10.1371/journal.pone.0339482.t002

HTD explanation

AI systems are often built on human-derived data, including human data of cognitive, affective, or behavioral nature. However, consumers often remain unaware of these human foundations. This lack of awareness reinforces the perception that AI systems are purely mechanical and devoid of human influence, which contributes to the issue of persisting AI resistance. Given this, making the human foundations of AI systems more visible is a promising way to explain how AI systems operate, while lifting resistance. This may be achieved through the provision of HTD explanation to end-users.

We define HTD explanation as the provision of a statement describing the role that humans play within a given AI system’s training source. This statement clarifies the origin and nature of the human data used to train the system, using language that is understandable to everyday users. For example, an AI-developed ready-made meals might be accompanied by the explanation: “These ready-made meals are developed by an AI system trained on millions of people’s food and taste preferences.” Similarly, an HTD explanation of how a self-driving car functions may read: “This car’s AI is trained on how millions of real drivers would react in similar situations.”

We propose that consumer response to the HTD explanation would be generally positive. As reasoned earlier, consumers show persistent resistance to AI technologies due to the perceptions of these technologies being lacking in inherently human qualities [11,13,14,18]. Yet, many of these technologies are fundamentally shaped by human data – a detail that many people overlook. By making the human contribution to AI more salient, HTD explanation may prompt consumers to reconsider their assumptions about AI, leading to more favorable perceptions and greater purchase intention for such technologies. Formally:

H1: HTD explanation positively influences purchase intention.

Perceived essence transfer (PET)

We anticipate HTD explanation to increase purchase intention through the mediating mechanism of perceived essence transfer (PET), grounded in contagion theory [51,52]. By highlighting the human origins of an AI system’s training data, HTD explanation brings human presence to the forefront. For example, the statement “this AI system uses customer taste and preferences data in developing our company’s ready-made meals” highlights the human sources (i.e., customers) and their input (i.e., taste and preferences) explicitly. This explicit link to human input may prompt consumers to infer that the AI system has absorbed the essence of human food preferences captured its training data. As a result, we anticipate this to shift consumers’ fundamental perceptions of AI systems from that of a foreign machine with no understanding of human food preferences to one that embodies human understanding of food preferences.

This whole effect can be explained through the principles of contagion. The contagion effect documents the phenomenon of PET between a source and a recipient that encounter each other [53]. It posits that immaterial qualities or the “essence” of a source can be transferred to a recipient through contact [54]. Specifically, it proposes that “...people, objects, and so forth that come into contact with each other may influence each other through the transfer of some or all of their properties” [53, p159]. These core qualities or “essence” are considered analogous to a soul or personal energy, which in this case is perceived to be transmitted from one source to a recipient [53]. PET effects have been observed to occur between human sources and objects [52,55], including clothing [56,57], personal belongings [58], artworks [59], and more. For example, people have been found to believe that the essence of an artist is captured in their artworks [59]. Possessions of celebrities have also been documented to fetch high prices due to the belief that their essence is contained in their personal belongings [58].

The contagion effect is characterized by three primary characteristics: permanence, holographic properties, and dose insensitivity [52]. The permanence principle asserts that perceived contact, however brief, will lead to the perception of enduring essence transfer from the source to the recipient [60]. This principle encompasses contacts that are considered both direct and indirect [52,55]. Direct contact concerns physical touch between two parties (e.g., a handshake, a hug), while indirect contact concerns symbolic contact between two parties (e.g., shared use of object) [55]. According to the permanence principle, once essence transfer through direct or indirect contact is perceived to occur, the recipient is regarded to be resistant to attempts of purification [52]. In other words, the recipient is perceived to be permanently changed following the contact [52,60]. The holographic principle posits that a small quantity of essence embodies most, if not all, of the characteristics of its source [60,61]. Finally, the dose insensitivity principle suggests that a minimal amount of essence transfer is sufficient to produce an all-encompassing perception of transfer of the source’s immaterial qualities and properties into the recipient [60]. Contagion operates in an all-or-nothing manner, in which only minimal contact between the source and recipient is required to produce a significant contagion effect, with prolonged contact generating only marginal additional contagion effect [52].

PET and AI technologies

In the case of AI technologies, what makes PET unique is that the contact between human and AI is symbolic, not physical. AI systems trained on human data never directly interact with their sources in a physical manner. Although AI systems never physically touch their human sources, symbolic contact has been documented to produce PET effects as effectively. Prior work shows that essence beliefs arise even from indirect traces such as photographs or handwriting [53,54]. In the case of AI, human data traces are not mere proxies but direct imprints of people’s thoughts, emotions, and behaviors, making them especially potent carriers of perceived essence. Unlike contagion effects observed with tangible objects such as clothing or artworks, the human essence is perceived to be transferred through large-scale data traces that represent human thoughts, emotions, and behaviors. The integration of human cognitive, emotional, and behavioral inputs creates a psychologically meaningful link between humans and the AI system. We expect HTD explanation to increase the salience of this connection, resulting in perceived symbolic contact between the two parties. In turn, we expect this to trigger contagion-based inferences, whereby human-like qualities are believed to be absorbed into the AI technology.

Another distinctive feature of the present context is that both human input and AI system are immaterial. Yet, their interaction can be understood through the same logic as material contagion. Just as essence is believed to transfer through physical contact between objects, symbolic contact between two immaterial entities (such as human data and AI systems) may trigger PET perceptions where human essence is believed to be transferred into the AI system. In both cases the process is the same: the essence of one entity is believed to be passed onto the other, whether through physical or symbolic contact. In sum, what makes PET unique in the AI context is that the “contact” is symbolic rather than physical. The scale of contact is vast, as millions of human datapoints may be seen as embedding diverse aspects of human essence into the system. Second, the form of contact is immaterial-to-immaterial, as human data and AI systems both lack physical substance. Together, these features distinguish PET in the AI domain from prior contagion phenomena.

Following perceived symbolic contact, contagion-based PET inferences will begin. The present case involves a no-contact, symbolic transfer between two immaterial entities: human data and AI systems. Human data operates as direct imprints of thoughts, emotions, and preferences. When an AI system is trained on such data, observers may perceive the system as having absorbed human essence, even without physical interaction. In line with contagion principles, this symbolic transfer is expected to be viewed as enduring (permanence), with even a small quantity of human input seen as embodying the whole (holographic), and any amount of transfer treated as sufficient to fully transmit the source’s essence (dose insensitivity) [60,61]. As a result, observers may come to perceive AI systems not as cold and impersonal machines, but as technologies that embody human qualities. By shifting these fundamental mental models, HTD explanation and PET processes may reduce consumer resistance to AI and increase willingness to adopt AI-enabled products and services. Formally:

H2: HTD explanation positively influences purchase intention through the mediating effect of perceived transfer of human essence.

Our conceptual framework is illustrated by Fig 1:

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Fig 1. Conceptual framework.

https://doi.org/10.1371/journal.pone.0339482.g001

Study 1: The effect of HTD explanation on purchase intention in car crash scenario

Results.

Logistic regression results indicate that the manipulation was successful with 91.8% correct classification (p < .001, B = 2.491). The Cronbach’s alpha scores indicate that all scales have good reliability (Purchase intention α= .971; Attitude α= .976).

We conducted an independent-samples t-test to investigate the effect of HTD explanation on purchase intention (H1). We found that HTD explanation has a significant positive effect on purchase intention, in which purchase intention is higher in the HTD condition relative to the control group, t(193) = 1.912, p = .029, M_HTD = 3.72, SD_HTD = 1.95, M_control= 3.21, SD_control= 1.77, Cohen’s d = .274). These results are also summarized in Fig 2.

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Fig 2. Study 1 means plot.

https://doi.org/10.1371/journal.pone.0339482.g002

We conducted a mediation analysis using Process Hayes Model 4 to test the role of PET in mediating the relationship between HTD explanation and purchase intention (H2). We decided to use Process Hayes to test our hypotheses as this analysis method allows us to test our predictions with bias-corrected bootstrapping, which is superior to earlier approaches such as the Baron and Kenny method [64]. Our analysis uses 5,000 bootstrap samples and a 95% confidence interval. The analysis results reveal a non-significant mediation effect, 95% CI [LLCI = −.0217, ULCI = .1406]). The direct effect of HTD explanation on purchase intention is marginally significant (p = .088; B = .221). The effect of HTD explanation on PET is non-significant (p = .187). The effect of PET on purchase intention is significant (p = .000, B = .286).

For robustness, we re-ran our main analysis while controlling for covariates. We first ran correlations to investigate potential covariates to the hypothesized relationship between HTD explanation and purchase intention. Specifically, we examined whether purchase intention is significantly correlated with gender, age, language, and pre-existing attitudes toward AI systems. We only included two genders (male and female) in the analysis, as the small number of participants in other categories (e.g., non-binary, prefer not to say) limited the feasibility of including them in the analysis. Out of the four variables, two variables are significantly correlated with purchase intention: pre-existing attitude (r = .664) and gender (r = −.161). We therefore included them as covariates in subsequent analysis. We ran a univariate ANOVA using purchase intention as the dependent variable, HTD explanation as the independent variable, and pre-existing attitude towards AI systems and gender as covariates. The effect of pre-existing attitude towards AI systems on purchase intention is significant (p < .001, partial η² = .435). The effect of gender on purchase intention is not significant (p = .117). The effect HTD explanation on purchase intention is still significant with the covariate controlled for (p = .006, F = 7.667, partial η² = .040. Participants who were given HTD explanation are more likely to express higher purchase intention relative to those in control condition.

We also conducted an independent-samples t-test to investigate the effect of HTD explanation on our exploratory dependent variable (perceived likelihood of stopping). The R1assumption of equal variances was met (p = .923; F = .009). We found that HTD explanation has a non-significant effect on perceived likelihood of stopping (p = .396, M_HTD = 68.95, SD_HTD = 24.46, M_control= 68.02, SD_control= 24.33, Cohen’s d = .038), and that there is no difference in perceived likelihood of stopping between the HTD and control groups.

Study 2: The effect of HTD explanation on PET and purchase intention in car crash scenario

Results.

Logistic regression results again indicate that the manipulation was successful with 88.4% correct classification (p < .001, B = 2.066). The Cronbach’s alpha scores indicate that all scales have good reliability (Purchase intention α= .945; Fear α= .940; Attitude α= .967).

We conducted an independent-samples t-test to investigate the effect of HTD explanation on purchase intention. The assumption of equal variances was not met, therefore the t-test conducted were heteroskedastic (p = .028; F = 4.849). We found that HTD explanation has a non-significant effect on purchase intention (p = .392, M_HTD = 3.71, SD_HTD = 1.87, M_control= 3.66, SD_control= 1.67, Cohen’s d = .032). The results indicate that there is no difference in purchase intentions between the HTD and control groups. These results are also visualized in Fig 3.

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Fig 3. Study 2 means plot.

https://doi.org/10.1371/journal.pone.0339482.g003

We conducted a mediation analysis using Process Hayes Model 4 to test the role of PET in mediating the relationship between HTD explanation and purchase intention (H2). Our analysis uses 5,000 bootstrap samples and a 95% confidence interval. The analysis results reveal a significant mediation effect, B = .076, SE = .039, 95% CI [LLCI = .0018, ULCI = .1546]). The direct effect of HTD explanation on purchase intention is non-significant (p = .622). The effect of HTD explanation on PET is significant (p = .05; B = .212). The effect of PET on purchase intention is significant (p = .000, B = .358).

For robustness, we re-ran our main analysis while controlling for covariates. We found three variables that are significantly correlated with purchase intention: gender (r = −.179), age (r = .228), pre-existing attitude towards AI systems (r = .606). Based on our correlations results, we ran a univariate ANOVA using purchase intention as the dependent variable, HTD explanation as the independent variable, and the following covariates: gender, age, pre-existing attitude towards AI systems. There were no significant effects of on purchase intention gender (p = .144). The effect of pre-existing attitude towards AI systems on purchase intention is significant (p = < .001, partial η² = .333). The effect of age on purchase intention is significant (p = .01, partial η² = .023). The main effect of HTD explanation on purchase intention remains non-significant (p = .294). Next, we re-ran our mediation analysis with covariates included. Our analysis uses 5,000 bootstrap samples and a 95% confidence interval. Pre-existing attitude has a significant effect on PET (p = .000, B = .427) and purchase intention (p = .000, B = .520). Gender has a marginally significant effect on PET (p = .058, B = .383) and purchase intention (p = .064, B = −.304). Age has a non-significant effect on PET (p = .291) and significant effect on purchase intention (p = .005, B = .015). The mediation analysis reveals significant full mediation effect when the covariate is controlled for, B = .039, SE = .020, 95% CI [LLCI = .0057, ULCI = .0826]. As in our main analysis, the direct effect of HTD explanation on purchase intention is non-significant (p = .632). The effect of HTD explanation on PET is significant (p = .016; B = .239). The effect of PET on purchase intention is significant (p = .001, B = .162).

Finally, we found a non-significant effect of HTD explanation on fear (p = .129, M_HTD = 4.23, SD_HTD = 1.62, M_control= 4.02, SD_control= 1.60, Cohen’s d = .131), with the assumption of equal variances met (p = .759; F = .094). The results indicate that there is no difference in fear between the HTD and control groups.

Study 3: The effect of HTD explanation on PET and purchase intention in ready-made meals scenario

Results.

Logistic regression results indicate that the manipulation was successful with 77.2% correct classification (p < .001, B = 1.554). The purchase intention scale has good reliability (Purchase intention α= .906).

We conducted an independent-samples t-test to investigate the effect of HTD explanation on purchase intention. The assumption of equal variances was met (p = .495; F = .467). We found that HTD explanation had a non-significant effect on purchase intention (p = .220, M_HTD = 4.67, SD_HTD = 1.31, M_control= 4.54, SD_control= 1.40, Cohen’s d = .094). The results indicate that there is no difference in purchase intentions between the HTD and control groups. These results are also summarized in Fig 4.

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Fig 4. Study 3 means plot.

https://doi.org/10.1371/journal.pone.0339482.g004

We conducted a mediation analysis using Process Hayes Model 4 to test our mediation hypothesis (H2). Our analysis uses 5,000 bootstrap samples and a 95% confidence interval. The analysis results reveal a significant full mediation effect, B = .047, SE = .025, 95% CI [LLCI = .0066, ULCI = .1066]. The direct effect of HTD explanation on purchase intention is non-significant (p = .839). The effect of HTD explanation on PET is significant (p = .012; B = .238). The effect of PET on purchase intention is significant (p = .000, B = .198).

For robustness, we re-ran our main analysis while controlling for covariates. We ran correlations to investigate covariates as in previous studies. We found two variables that are significantly correlated with purchase intention: gender (r = −.134), pre-existing attitude towards AI systems (r = .389). These two variables were then used in our subsequent analysis as covariates. We ran a univariate ANOVA using purchase intention as dependent variable, HTD explanation as the independent variable, and the following covariates: gender, pre-existing attitude towards AI systems. The effect of gender on purchase intention is non-significant (p = .107). The effect of pre-existing attitude towards AI systems on purchase intention is significant (p = < .001, F = 3.595, partial η² = .210). The effect HTD explanation on purchase intention remains non-significant (p = .375). Next, we also re-ran our mediation analysis (5,000 bootstrap samples and a 95% confidence interval) with pre-existing attitude towards AI systems included as a covariate. Gender was excluded as a covariate due to its non-significant effect in prior analysis. Pre-existing attitude has a significant effect on PET (p = .000, B = .335) and purchase intention (p = .000, B = .381). Our analysis results indicate that the mediation effect weakens and becomes marginal when controlling for covariate effects, B = .029, SE = .019, 95% CI [LLCI = −.0002, ULCI = .0725]. The direct effect of HTD explanation on purchase intention is non-significant (p = .481). The effect of HTD explanation on PET is significant (p = .006; B = .253). The effect of PET on purchase intention is significant (p = .024, B = .114).

Study 4: Pre-existing trust as a moderator in effect of HTD explanation on adoption intention in aged care scenario

Results.

Logistic regression results indicate that the manipulation was successful with 93.4% correct classification (p < .001, B = −2.642). The Cronbach’s alpha scores indicate good reliability for all scales (Adoption intention α= .975; Trust α= .884).

We conducted a moderation analysis using Process Hayes Model 1 to test our moderation hypothesis (H3). Our analysis uses 5,000 bootstrap samples and a 95% confidence interval. The analysis results reveal a marginally significant negative moderation effect (p = .08; B = −.085). A Johnson-Neyman analysis revealed that the effect of HTD explanation on purchase intention was significant and negative when baseline trust in AI technologies was greater than 8.24 (measured on a 9-point semantic differential scale), B = −.264, SE = .135, p = .05, 95% CI [−.530,.002]. This suggests that for consumers with high prior trust in AI, HTD explanation may backfire and reduce their intention to purchase AI-generated products. The Johnson-Neyman moderation plot is given in Fig 5.

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Fig 5. Study 4 Johnson-Neyman moderation plot.

https://doi.org/10.1371/journal.pone.0339482.g005

For robustness, we re-ran our main analysis while controlling for covariates. We ran correlations to investigate covariates as in previous studies. We found two variables that are significantly correlated with adoption intention: perceived empathy (r = .778) and gender (r = .106) and thus we used them as covariates (age and language are not significantly correlated with adoption intention). We re-ran our moderation analysis with covariates included. Our analysis uses 5,000 bootstrap samples and a 95% confidence interval. Perceived empathy has a significant effect on adoption intention (p = .000; B = .936). Gender has a non-significant effect on adoption intention (p = .754) The interaction between HTD explanation and pre-existing trust in AI systems on adoption intention is marginally significant and negative with the covariate controlled for (p = .071; B = −.068). Probing the interaction, our Johnson-Neyman output reveals that the effect of HTD explanation on purchase intention is marginally significant and positive for people with low pre-existing trust in AI technologies. Specifically, the interaction is marginally significant and positive for baseline trust levels of 1.00 (p = .078, B = .341, SE = .193, 95% CI [−.038,.792]) to 2.90 (p = .094, B = .212, SE = .126, 95% CI [−.037,.460]). Although marginal, these findings suggest that HTD explanation does improve adoption intention for individuals with low pre-existing trust in AI technologies when individual differences are controlled for.

Discussion.

Our results provide marginal support for our moderation hypothesis (H3), in which pre-existing trust in AI systems moderates the effect of HTD explanation on adoption intention. Our findings indicate that HTD explanation has a marginal effect in boosting (reversing) the positive effects of HTD explanation on adoption intention for individuals with low (high) pre-existing trust in AI technologies. These findings present important implications for researchers and practitioners considering using HTD explanation as a means to improve AI adoption or purchase intentions. Our study results are summarized in Table 3. We now close this section and discuss our findings.

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Table 3. Summary of study results.

https://doi.org/10.1371/journal.pone.0339482.t003

Theoretical implications

This paper presents three key contributions. First, we introduce and conceptualize HTD as a psychologically meaningful feature of AI systems that may shape consumer response to AI technologies. While training data is foundational to AI development, prior work has predominantly examined it from technical or computational perspectives. We extend this conversation into the consumer domain by proposing HTD as an explainable characteristic that can be communicated to end-users and shape perceptions of AI technologies. In doing so, we also introduce HTD explanation as a form of source-based XAI, one that emphasizes the human origins of an AI system’s training data rather than its inner workings or decision logic. In turn, this offers a way to address core concerns around AI’s perceived lack of humanness by making its human foundations more visible. While the empirical support is preliminary, this study offers a starting point for future work at the intersection of AI explainability and consumer behavior.

Second, we extend the concept of PET to the domain of human-AI interaction. Drawing from the contagion literature [51,52], we suggest that consumers may perceive a symbolic transfer of human essence into AI systems when informed that the system was trained on human data. While this psychological process has previously been examined in the context of physical objects and interpersonal interactions [52,53], we apply it here to a non-physical, data-driven context. Our findings offer initial empirical support for PET as a potential mechanism through which consumers interpret AI systems as possessing humanlike qualities.

Building on this mechanism, we explore what may be described as deep humanization effects. Through PET, consumers may build the perception that an AI system has internalized core human qualities, such as thought, emotion, or judgment, through its training process. This form of humanization reflects an attribution of humanness that is internalized, grounded in the belief that human essence has transferred into the technology. By contrast, surface humanization arises from more superficial perceptual cues, such as the presence of a human face, voice, or conversational style [3–5]. These cues can make an AI appear human-like, but they do not alter consumers’ inferences about the system’s underlying essence of being machine-like. This distinction also reflects different psychological routes of humanization. Surface humanization is driven by external resemblance and often triggers heuristic judgments based on appearance or behavior. In contrast, deep humanization is driven by causal inferences about the human origins of AI training data. In this research, we found initial evidence of consumers reasoning that if AI has been trained on human data, it must carry forward human essence that is transferred into the AI recipient. While surface humanization may shape perceptions of human-likeness, deep humanization suggests a more profound reappraisal of what an AI system is, implying that the technology may embody qualities once thought to be uniquely human. While our evidence is preliminary, it raises the possibility that perceptions of humanness can emerge not only from how AI looks or behaves, but also from how it is made. Overall, our findings suggest that PET may serve as a promising but context-dependent mechanism through which HTD explanation influences consumer perception by creating deep humanization effects. These preliminary findings also add nuance to existing work on anthropomorphism by highlighting that humanization effects are multifaceted, and can operate at different levels of psychological depth.

Third, we present early evidence that consumer responses to HTD explanation may vary depending on pre-existing trust in AI technologies. The effects of HTD explanation on adoption intention appear to be marginally positive for individuals with low pre-existing trust in AI technologies. This suggests that HTD explanation does have some efficacy to lower AI resistance and increase adoption intentions for individuals who hold negative baseline perceptions towards these technologies. However, our results show that HTD explanation could backfire for individuals with very high trust in AI, potentially lowering their intention to adopt AI-generated products. While the interaction effects warrant further investigation, it raises the possibility that explainability strategies are not universally effective and may be sensitive to individual differences. This underscores the importance of considering audience characteristics when designing communication about AI systems.

Managerial implications

The presence of PET as an underlying mechanism indicates that managers have an opportunity to strategically emphasize the human origins of AI training data to shape the perceptions of a deeply humanized AI technology among consumers. However, the downstream effects on purchase and adoption intentions appear to be context-dependent and limited across consumer groups. While our research provides some evidence for HTD explanation’s efficacy in creating deep humanization effects via PET, its net impact on behavior-level outcomes is limited, potentially due to mixed reactions from consumers.

This variation in consumer interpretation means that the practical benefits of HTD explanation may be conditional. Our findings provide tentative evidence that HTD explanation could resonate more with consumers who hold lower baseline trust in AI technologies, while it may have negative impact among those with higher pre-existing trust in AI technologies. Our results highlight the importance of audience segmentation and cautious message tailoring when experimenting with HTD-based communication strategies in AI-driven product contexts. Managers seeking to make use of this strategy need to pilot test HTD messaging in segmented campaigns. This may entail running controlled tests where different versions of HTD explanations (alongside a condition without HTD explanation) are presented across audience segments. Following this, managers may then compare consumer response across segments, including their click-through rates, purchase rates, product trial uptake, and other behavioral outcomes of interest. Pilot tests may also incorporate different framings of HTD explanation. This may entail providing variations in information on the expertise, diversity, or representativeness of the human data sources to identify which angle produces the most positive outcomes. This recommendation offers managers a set of actionable steps to experiment with HTD explanation in practice while also creating opportunities to capitalize on it if a certain framing proves effective.

Societal and ethical implications

Beyond theoretical and managerial outcomes, our findings point to societal implications for how AI technologies are perceived and adopted. We set out to examine whether HTD explanation could offer a solution to the ongoing problem of consumer resistance toward AI technologies. However, what we found instead is a more complex picture. Specifically, we found public reactions to HTD explanations to be non-uniform and may depend on individuals’ differences (e.g., differences in pre-existing trust in AI technologies). This variation points to a broader societal tension: while increasing awareness about AI’s human underpinnings could help reduce AI resistance in some segments, it may simultaneously disrupt favorable perceptions in others. In practical terms, this implies that HTD explanation could shape public discourse and perceptions of AI technologies in uneven ways. As AI technologies continue to permeate everyday life, understanding how people interpret the humanization of AI systems becomes an important societal concern, warranting further research to explore these complexities. For policymakers, this underscores the need to carefully test and frame human-data narratives, ensuring that communication strategies do not unintentionally polarize public attitudes toward AI technologies. For example, if governments introduce public-sector AI (e.g., welfare eligibility tools) and emphasize that these systems are trained on citizen data, such messaging may be received differently across audiences, with no guarantee of uniform acceptance. This suggests that policy communication about HTD use should proceed cautiously, with small-scale trials used to assess reactions before broader implementation.

Our findings also connect to ongoing debates in AI ethics. Ethical debates on transparency and explainability include concerns about AI’s data sources. For managers, this means that deploying HTD explanation is not only a communication choice but also an ethical one. For example, describing an aged care companion robot as trained on real human aged care workers may enhance transparency and the explainability of this technology. However, it may also raise the ethical dilemma of whether companies should disclose information that they know could divide audiences. The tension here lies between two principles: on one hand, transparency is a core tenet of XAI and ethical AI practices [19,25]; on the other, disclosure that triggers resistance in some groups may undermine the broader goal of building public trust in AI technologies. More than that, firms may become unwilling to disclose such information altogether, which could reduce transparency and work against broader calls for explainable AI. For policymakers, these findings underscore the importance of developing guidance on when and how firms should disclose the use of human training data through HTD explanation, ensuring that disclosure practices build trust without creating new forms of resistance.

Limitations and future research

We now note our study limitations and direction for future research. First, our studies relied on self-reported measures of purchase or adoption intention, which may not always translate into actual behavior. While intention is a widely used proxy in consumer research, we acknowledge the absence of behavioral measures or field experiments as a key limitation in our paper. This limitation highlights the need for future studies to incorporate behavioral measures or field experiments to test ecological validity. This limitation also reflects broader constraints in developing field-ready, controlled HTD manipulations. Embedding experimentally varied HTD explanations into various AI systems would require backend access, systematic control over explanation delivery, and the ability to track downstream consumer behavior. These conditions make it technically and logistically challenging to run such studies. In saying this, we acknowledge that a field study would help validate the ecological relevance of our findings. Provided the necessary infrastructure is available to researchers, future research should incorporate behavioral outcomes or field experiments. These experiments should assess whether the psychological effects of HTD explanation extend to real-world consumer choices and purchase behavior, thereby establishing the ecological validity of our framework.

Second, given the small mediation effects we observed, future research may examine factors that may have attenuated the PET effect in the current studies. As discussed earlier, these may include the presence of moderators such as prior trust, which was then examined in Study 4. Another potential moderator relates to the variability in how human sources are perceived by users. Differences in perceived source favorability may lead participants to interpret the essence transfer as either positive or negative, potentially balancing out the overall effect. Future studies could build on these insights by manipulating these moderating factors to assess their influence on the strength and direction of the PET pathway.

Third, given the non-uniform effects of HTD explanation, future studies would also benefit from including additional control variables that may shape these responses. This may include controlling for prior AI exposure, AI usage experience, technological literacy and knowledge, and other cultural factors that may contribute to individual differences. Future research could also explore how variations in the perceived nature, quality, diversity, or credibility of HTD source moderate consumer response to HTD explanation. This exploration may reveal additional factors that may have played a role in the observed inconsistencies in our findings. Additionally, as the moderation effect of prior trust was marginal, future research could validate the stability of this effect by increasing sample size or adjusting the HTD disclosure method (e.g., altering its framing, length, or level of detail) to test whether the effect generalizes across different presentation formats, thereby strengthening the preliminary nature of the current conclusions.

Fourth, we also recommend testing our conceptual framework in other contexts. One factor that may have contributed to our present findings is the depiction of uncommon technologies in our experimental scenarios. While the media has increased consumers’ knowledge of self-driving cars, this technology is still not commonplace and is still going through many trials in the present. As such, study participants may struggle to picture themselves purchasing or using these technologies regardless of the training data used, hence contributing to the small means difference observed in our studies. Future research may use AI technologies that are more commonplace for further testing. This includes customer service chatbots, virtual assistants, and smart home devices. These technologies are widely available and accepted, providing a more relatable foundation for the future experiments.

Next, we recruited participants primarily through online panels and, in one study, through a university student sample. While useful for early-stage theory testing, the generalizability of these findings to broader consumer populations remains limited. Future work should replicate and extend our study using more diverse samples to test the robustness of the effects. In particular, we recommend broadening demographic and cultural diversity in forthcoming studies. Future work should employ more diverse and representative samples, incorporate behavioral and field experiments, and systematically account for these boundary conditions to strengthen the robustness of the findings.

Finally, while this paper focuses on HTD as a general feature of AI systems, human training data can take on different forms, e.g., cognitive, affective, or behavioral HTD. Future research may explore whether these HTD types have additive or distinctive effects on purchase and adoption intentions depending on the AI context. For example, affective data may have a stronger impact in high-emotion-demand contexts (e.g., mental health support AI tools). Cognitive HTD may be more influential in decision-driven or analytical domains (e.g., strategic planning, managerial decision support systems). Behavioral HTD may be seen more positively in action-oriented contexts that rely on human performance or behavioral data (e.g., fitness coaching, physical rehabilitation guidance). Exploring these nuances would deepen understanding of how different facets of human data shape AI perceptions and consumer responses.