# Identifiable victim effect in charitable giving and marketing

## Introduction to the Identifiable Victim Effect

The allocation of philanthropic resources frequently defies normative economic models and consequentialist ethics. From a strictly utilitarian standpoint, the distribution of aid should scale proportionally with the magnitude of human suffering; a systemic crisis threatening millions of lives should rationally command exponentially more resources than an isolated emergency threatening a single individual. However, human behavioral responses consistently diverge from this mathematical logic. This divergence is most acutely captured by the identifiable victim effect (IVE), a psychological phenomenon wherein individuals exhibit a disproportionately greater willingness to provide aid to a specific, identifiable victim than to a large, vaguely defined, or statistically abstract group facing identical or greater hardship [cite: 1, 2, 3, 4].

First conceptualized by American economist Thomas Schelling in 1968, the phenomenon rests on the observation that the peril of a specific person invokes profound emotional responses—such as anxiety, sentiment, guilt, and awe—while the aggregation of suffering into "statistical death" effectively neutralizes these affective triggers [cite: 4, 5]. The concept is often colloquially summarized by quotes attributed (sometimes apocryphally) to historical figures; for instance, the assertion that "one death is a tragedy; a million deaths is a statistic," or Mother Teresa’s remark, "If I look at the mass I will never act. If I look at the one, I will" [cite: 4, 6].

Subsequent decades of behavioral economics and moral psychology research, pioneered by scholars such as Jenni and Loewenstein (1997) and Small and Loewenstein (2003), sought to formalize this intuition [cite: 2, 3, 7]. Their foundational laboratory experiments demonstrated that providing a victim's name, age, and photograph generated significantly higher donation amounts than appeals relying on macro-level statistics regarding famine or disease [cite: 3, 4]. In the context of charitable giving and cause-related marketing, the identifiable victim effect has historically been treated as a foundational heuristic. Nonprofit organizations routinely design fundraising campaigns around singular narratives, utilizing vivid imagery and personal testimonies to bridge the psychological distance between the donor and the beneficiary [cite: 8, 9]. 

However, the reliability, universality, and ethical implications of this effect have recently become subjects of intense academic scrutiny. High-powered replication attempts conducted between 2023 and 2026 have challenged the robustness of the identifiable victim effect, suggesting that its influence may be highly conditional, susceptible to publication bias, and bounded by cultural, contextual, and cognitive variables [cite: 7, 10, 11]. This report provides an exhaustive analysis of the identifiable victim effect, exploring its underlying psychological mechanisms, synthesizing recent empirical debates regarding its validity, examining cross-cultural variations, and evaluating its strategic deployment in modern cause-related marketing and digital fundraising.

## Psychological Mechanisms of Victim Identification

The tendency to privilege identifiable victims over statistical aggregates is not the result of a single cognitive error, but rather the convergence of multiple psychological processes. These mechanisms operate primarily within the framework of dual-process theory, which posits that human cognition relies on two parallel systems: System 1 (rapid, intuitive, and affect-driven) and System 2 (slow, deliberative, and analytical) [cite: 7].

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### Affective Processing and Emotional Contagion

The primary driver of the identifiable victim effect is the affect heuristic, wherein individuals make decisions based on immediate emotional responses rather than objective calculations of utility [cite: 4]. When presented with an identifiable victim—complete with a face, a name, and a personal narrative—donors experience rapid emotional contagion. Visual cues, such as a sad facial expression, automatically trigger physiological and neural responses in the observer, evoking distress, sympathy, and a desire to alleviate the victim's suffering [cite: 12, 13]. 

Research indicates that emotions like guilt and empathic concern are central mediators in the donation process [cite: 1, 5, 7]. Statistical representations of suffering fail to engage this affective circuitry. Large numbers are cognitively processed by System 2, which, while capable of understanding the objective severity of a crisis, does not generate the visceral emotional arousal necessary to prompt immediate prosocial behavior [cite: 7, 8]. Furthermore, experimental interventions that instruct participants to engage in deliberate, analytical calculation prior to evaluating a charitable appeal have been shown to suppress affective responses, thereby eliminating the identifiable victim effect and reducing overall giving [cite: 8, 14, 15].



### Proportion Dominance and Perceived Impact

Beyond raw emotion, the identifiable victim effect is heavily influenced by the donor's perception of their own efficacy. Proportion dominance refers to the cognitive bias where individuals assess the value of an intervention not by the absolute number of lives saved, but by the proportion of the reference group that is rescued [cite: 5, 16]. 

When a campaign features a single, identifiable victim, the "reference group" is reduced to one. A donor perceives that their contribution can completely solve the problem, achieving a 100% success rate within that micro-context [cite: 4, 5]. Conversely, when a campaign presents statistics—such as 26 million children facing starvation—the reference group is vastly expanded [cite: 17]. Even a substantial donation is cognitively processed as saving only a fractional percentage of the total victims, inducing a sense of futility. Donors become averse to interventions that feel like a "drop in the ocean," leading to decreased participation [cite: 18, 19]. As Dickert, Sagar, and Slovic demonstrated, statistics and the analytical mode of thinking they rely on systematically lessen the emotional effect of appeals and negatively impact giving levels [cite: 8].

### The Singularity Effect and Compassion Fade

Closely related to the identifiable victim effect is the "singularity effect," which dictates that individuals are more inclined to help a single identified victim than a group of identified victims [cite: 4, 11, 20]. Research by Paul Slovic and colleagues documented the phenomenon of "compassion fade" or "psychophysical numbing." In these studies, subjects demonstrated the highest levels of sympathy and financial generosity when presented with one starving child [cite: 4, 17]. 

However, when a second equally identifiable child was introduced to the appeal, emotional responses and donation amounts paradoxically declined [cite: 17]. For example, in experiments comparing responses to one versus two victims, subjects reported less willingness to donate and felt their hypothetical donation ($1.31 on average) would make less of a difference in the two-victim scenario [cite: 17]. This suggests that the human capacity for empathy does not scale additively. The introduction of multiple victims dilutes the vividness of the single narrative, fragments the donor's attention, and begins to shift cognitive processing away from the affective System 1 and toward the analytical System 2, ultimately dampening the impulse to give [cite: 7, 11].

## Methodological Scrutiny and Replication Failures

While the identifiable victim effect achieved canonical status in behavioral economics during the early 2000s, the period spanning 2023 to 2026 has witnessed a severe destabilization of its empirical foundations. Driven by the broader credibility revolution in psychology, researchers have subjected the seminal findings to high-powered, pre-registered replication attempts, yielding highly contested results that suggest the effect is fragile and heavily bounded [cite: 10].

### Reevaluations of Meta-Analytic Evidence

The initial meta-analysis of the identifiable victim effect, conducted by Lee and Feeley (2016), analyzed 41 effects from 22 experiments and concluded that the overall effect was statistically significant but practically modest ($r = 0.05$) [cite: 5, 11]. The analysis revealed that the identification of victims resulted in gains in helping when a single victim was described, but actually decreased helping when a group of victims was presented in the identified conditions [cite: 5]. 

However, subsequent methodological scrutiny has challenged even this modest baseline. In 2023, Maier et al. reanalyzed the Lee and Feeley dataset using updated, robust Bayesian methods designed to detect and adjust for publication bias [cite: 10, 11]. Publication bias—the tendency for academic journals to publish statistically significant positive results while discarding null findings—was found to heavily skew the historical data. Upon adjusting for this bias, Maier et al. uncovered "moderate evidence of publication bias and strong evidence for the absence of an identified victim effect" ($BF_{01} = 14.93$). They revised the model-averaged mean effect size estimate down to a negligible $r = 0.002$ (95% CI [0; 0.004]) [cite: 11, 20].

### Laboratory and Conceptual Replication Shortfalls

In tandem with meta-analytic reevaluations, multiple teams of researchers have failed to replicate the specific foundational experiments that established the identifiable victim effect and the singularity effect. Maier et al. (2023) conducted an unsuccessful conceptual replication of the seminal Small, Loewenstein, and Slovic (2007) study, finding no support for the identified victim effect [cite: 10, 20]. 

Similarly, Majumder et al. (2024) attempted a pre-registered, highly powered replication of Kogut and Ritov's classic 2005 and 2007 studies. The replication found no support for main effects or interactions between singularity and identifiability; participants indicated roughly equal levels of distress, empathy, and willingness to contribute regardless of whether they were presented with a single identified victim, a group of identified victims, or unidentified victims [cite: 10]. 

Furthermore, Moche, Karlsson, and Västfjäll (2024) conducted five studies across nearly 8,000 participants examining the IVE alongside a novel concept called "unit asking." They found an inverse effect: participants seeing an identified appeal actually donated significantly less ($M = 55.51$) than participants seeing an unidentified appeal ($M = 70.70$) [cite: 21]. The authors observed that while identifiability influenced self-reported affective reactions (donors felt higher levels of personal distress), this emotion did not translate into increased donation amounts, effectively severing the proposed causal link between victim identification and actual financial giving [cite: 21].

### Divergence in Large-Scale Field Experiments

The fragility of the identifiable victim effect is further highlighted by its frequent failure to manifest in real-world field experiments. While laboratory settings often force a direct cognitive juxtaposition between identifiable and statistical appeals, natural environments introduce complex contextual variables such as brand loyalty, budget constraints, and background noise.

In a massive natural field experiment conducted by Lesner and Rasmussen (2014) in Denmark, direct mail solicitations were sent to 25,797 prior donors of a nonprofit charity [cite: 16]. The campaign tested three variations: a letter focusing on one identifiable victim, one focusing on statistical information, and a control. The study found no significant difference in the size or frequency of donations between campaigns featuring an identifiable victim and those featuring statistical victims [cite: 16]. 

Similarly, research conducted by Faunalytics (2019) testing donation appeals for animal charities found no significant differences in conversion rates or donation amounts between identifiable victim appeals, statistical appeals, or minimal text appeals lacking either element [cite: 22]. Participants gave an average of 16.4 cents to companion animals regardless of whether an identified victim or statistical framing was used. The researchers concluded that minimal appeals were the most cost-effective messaging strategy, as lengthier ads containing vivid narratives or data did not improve financial outcomes [cite: 22].

| Study / Authors | Methodology & Scope | Key Findings Regarding the Identifiable Victim Effect (IVE) |
| :--- | :--- | :--- |
| **Small, Loewenstein, & Slovic (2007)** | Lab Experiment | Established IVE; identifiable victims generated significantly higher donations than statistical victims [cite: 3, 4]. |
| **Lesner & Rasmussen (2014)** | Natural Field Experiment (N=25,797) | Failed to find an IVE; identified victim letters did not yield higher donations than statistical letters [cite: 16]. |
| **Lee & Feeley (2016)** | Meta-Analysis (41 effects, 22 studies) | Found a statistically significant but weak IVE overall ($r = 0.05$) [cite: 5, 11]. |
| **Maier, Wong, & Feldman (2023)** | Bayesian Reanalysis of Meta-Data | Found publication bias; adjusted IVE effect size fell to a statistically negligible $r = 0.002$ [cite: 10, 11]. |
| **Moche, Karlsson, & Västfjäll (2024)** | Lab Experiments (N=7,996) | IVE absent in donations; identified appeals yielded lower donations despite higher self-reported distress [cite: 21]. |

## Cultural and Cognitive Moderators of Donor Behavior

The inconsistent replication of the identifiable victim effect points to its nature as a highly context-dependent phenomenon rather than a universal cognitive law. The efficacy of identifiable narratives is strictly bounded by cultural dimensions, group identity, and the cognitive state of the donor.

### Collectivism, Individualism, and Self-Construal

The original studies establishing the identifiable victim effect were conducted almost exclusively within Western, Educated, Industrialized, Rich, and Democratic (WEIRD) populations, which lean heavily toward individualism [cite: 1, 23, 24]. In individualist cultures, personal narratives resonate deeply because the cultural schema prioritizes individual autonomy, unique attributes, and personal rights [cite: 23, 25].

When researchers have examined the effect in highly collectivist cultures, the dynamics shift considerably. Collectivist cultures emphasize interdependence, group harmony, and shared social identity over individual distinctiveness [cite: 23, 25]. Consequently, the singularity effect (the preference for a single victim over a group) often dissipates in these contexts. Studies have shown that individuals with higher collectivist values exhibit an increased willingness to contribute to a group of victims, perceiving the group as a cohesive, single entity worthy of support [cite: 26, 27]. 

However, identifiability still plays a role in collectivist environments. Research within Chinese charitable contexts demonstrates that the identifiable victim effect leads to significantly stronger intentions for *collective* donation actions, provided the appeal increases both donors' emotional reactions and their culturally ingrained sense of perceived responsibility to the broader social fabric [cite: 1, 2]. Furthermore, cross-cultural studies utilizing the concept of self-construal show that the identifiable victim frame effectively increases donation intent for individuals with an interdependent self-construal, driven by their heightened holistic thinking style [cite: 28].

### Social Distance and the Insider Victim Effect

The identifiable victim effect is severely constrained by social distance and group identity. The reduction of psychological distance achieved by naming and showing a victim is frequently insufficient to overcome deep-seated out-group biases [cite: 29, 30].

Research into the "insider victim effect" demonstrates that individuals are substantially more empathetic and generous when the identifiable victim shares their national, cultural, or social identity [cite: 10, 29, 31]. In high-powered replications, scholars found that while the identifiable victim effect might hold for in-group victims, it evaporates or even reverses when the victim belongs to an out-group. Specifically, willingness to contribute, empathic concern, and distress were recorded at significantly lower levels for identified out-group victims compared to unidentified victims or in-group victims [cite: 10]. If a donor feels no shared social identity with the beneficiary, the vivid details of the victim's life may paradoxically highlight their "otherness," reinforcing social distance rather than bridging it.

### Emotion Regulation and Baseline Mood

The internal state of the donor at the moment of solicitation serves as another critical moderator. Because the identifiable victim effect relies on negative emotional contagion—specifically distress, guilt, and sadness—it is highly vulnerable to the donor's baseline mood [cite: 4, 13]. 

Studies by Sabato and Kogut (2021) have shown that inducing a positive mood in potential donors prior to an appeal attenuates the identifiable victim effect [cite: 13]. When donors are in a positive state of mind, they are less susceptible to the negative emotional pull of a single tragic narrative. In these conditions, statistical or generalized appeals perform equally well or better. For instance, in their positive mood condition, donations to the general need ($M = 51.46$) and to the single identified victim ($M = 42.50$) did not differ significantly, completely dissolving the IVE [cite: 13, 27].

Additionally, individual differences in cognitive processing styles dictate susceptibility to the effect. Individuals who naturally favor analytic, "rational" processing (measuring high in "need for cognition") demonstrate no difference in donation amounts when presented with identifiable versus statistical victims [cite: 14]. The identifiable victim effect is therefore predominantly observed among individuals who default to heuristic, emotionally driven decision-making [cite: 14].

## Applications in Cause-Related Marketing and Fundraising

Despite academic debates regarding the precise statistical magnitude of the identifiable victim effect, the core principles of identifiability remain deeply entrenched in the applied practice of cause-related marketing (CRM) and digital fundraising. Marketers continually operationalize identifiability through visual media, storytelling, and digital metrics to drive conversion.

### Visual Identifiability in Digital Crowdfunding

In digital marketing, the face of the beneficiary is utilized as the primary mechanism for reducing anonymity. Visual media is inherently more effective than text at triggering the rapid affective responses required for the identifiable victim effect [cite: 17, 19]. The use of compelling, often distressing visual imagery in international development—colloquially referred to as "poverty porn"—remains a controversial but highly utilized tactic. 

Research indicates that appeals utilizing negative, personalized images of individuals in despair consistently generate more pro-social giving behavior and public support for foreign aid than descriptive statistical information regarding global poverty [cite: 32]. The psychological mechanism relies on visual identifiability breaking through cognitive numbing; a single picture of a helpless individual removes the anonymity of the enterprise and creates immediate psychological proximity [cite: 32, 33]. 

Recent large-scale data analysis supports the applied value of visual identifiability over strict textual narratives. A 2026 field study by Morvinski and Gordon-Hecker analyzed over 645,000 real-world crowdfunding campaigns to test the "visual identifiable victim effect" independently of narrative text [cite: 33]. The study found that campaigns featuring at least one identified human face had a higher success rate (82.3%) of reaching their funding goal compared to campaigns lacking identified human faces (81.5%) [cite: 33]. While this percentage difference appears marginal in isolation, across thousands of campaigns, it represents a statistically significant ($p < .001$) and financially substantial advantage for visual identifiability [cite: 33]. However, mirroring the recent replication failures of the singularity effect, the study found no evidence that campaigns featuring a *single* identified face outperformed those featuring *multiple* identified faces [cite: 33].

### Conversion Metrics and Email Campaign Optimization

In the context of digital fundraising, the success of identifiable narratives over statistical appeals is routinely measured through standard Key Performance Indicators (KPIs) such as Open Rates, Click-Through Rates (CTR), and Conversion Rates. Sector-wide benchmarking indicates that highly personalized, narrative-driven email campaigns achieve significantly higher engagement than generic appeals. 

According to M+R Benchmarks for the nonprofit sector, organizations aim for email open rates of 25%, CTRs of 2.5%, and final conversion rates near 1% [cite: 34, 35, 36]. Marketers utilizing donor segmentation to deliver specific, identifiable stories matched to donor interests (e.g., segmenting dog stories versus cat stories for animal shelters) report open rates up to 14.32% higher and click-through rates up to 100.95% higher than non-segmented, generic campaigns [cite: 34, 35]. While the base conversion rate of an email newsletter may hover around 1%, optimizing the landing page to feature cohesive narratives and visually identifiable beneficiaries has been documented to increase conversion rates by up to 300% (e.g., from 1.2% to 4.8%) with the same ad spend [cite: 35, 37].

| Key Performance Indicator (KPI) | Nonprofit Sector Target Average | Impact of Narrative Personalization / Identifiability |
| :--- | :--- | :--- |
| **Email Open Rate** | 25% | Segmented/Personalized lists see open rates up to 14.32% higher than generic appeals [cite: 34, 35]. |
| **Click-Through Rate (CTR)** | 2.5% | Contextual narratives can yield click-throughs up to 100.95% higher than non-segmented campaigns [cite: 34, 35]. |
| **Landing Page Conversion Rate** | 1.0% | Aligning ad narrative with optimized landing page elements can yield increases in conversion up to 300% [cite: 35, 37]. |
| **Donor Retention Rate** | 45% | Transparent impact reporting mapped to specific beneficiaries boosts retention [cite: 34, 38]. |

## Strategic Campaign Structuring and Impact Reporting

To navigate the tension between the ethical limitations of relying solely on emotional distress and the proven ineffectiveness of raw statistics, sophisticated nonprofit brands have developed hybrid communication models. These strategies merge the affective power of the identifiable victim effect with the deliberative requirements of institutional donors.

### The Hybrid Model of Impact Reporting

Modern impact reports utilize the identifiable victim effect as a structural pillar. Best practices for high-conversion annual reports in the nonprofit sector dictate a synthesis of emotional narrative and empirical data. Reports typically feature a "Year in Numbers" dashboard to satisfy the analytical requirements of institutional funders, showcasing key metrics and Social Return on Investment (SROI) calculations (e.g., "£1 invested = £4.50 social value") [cite: 39, 40]. 

This quantitative framing is paired intimately with a deeply detailed "Beneficiary Story"—a single, named, identifiable narrative that contextualizes the data [cite: 40]. This hybrid approach effectively targets both System 1 and System 2 cognitive processes, mitigating the risk of compassion fade while satisfying the donor's need for verified impact. Marketers frequently refer to the "30% Rule," suggesting that while automated systems or AI can compile 70% of the statistical data, the remaining 30% must be human-curated strategy focusing heavily on the identifiable victim effect to bridge the gap between data and emotion [cite: 40].

### Case Studies in Narrative Empathy

The nonprofit organization Charity: Water serves as a primary case study in the successful, ethical deployment of the identifiable victim effect. Rather than relying on overwhelming statistics—intentionally shifting their messaging from aggregate deficit frames like "1 in 10 people lack water" to focus on individual impact—the organization builds its brand around vivid, high-quality visual storytelling focused on specific beneficiaries [cite: 41, 42]. 

This strategy operationalizes the "identifiable beneficiary effect," pivoting away from victimhood toward empowerment [cite: 6, 9]. By sharing specific names, GPS coordinates of constructed wells, and photographs of communities, the organization reduces psychological distance and builds immense trust [cite: 38, 40]. This approach triggers positive neurochemical responses, such as oxytocin release, by fostering a sense of shared purpose and connection between the donor and the specific community receiving aid [cite: 41]. 

A poignant real-world example of this efficacy was the 2011 campaign inspired by Rachel Beckwith. The nine-year-old launched a birthday campaign on Charity: Water's platform to raise $300. Following her tragic death, the identifiable narrative of her compassion combined with the identifiable victims she sought to help generated immense emotional contagion. The campaign ultimately raised $1.2 million, funding clean water for 37,770 people in Ethiopia [cite: 41]. By linking individual donor narratives directly to identifiable beneficiary outcomes, organizations like Charity: Water report exceptional donor retention rates approaching 75% [cite: 38].

## Artificial Intelligence and Algorithmic Bias

As the nonprofit sector and marketing industries increasingly integrate Artificial Intelligence (AI) for tasks ranging from campaign copywriting to automated grant evaluation, the identifiable victim effect presents a novel technological challenge. Large Language Models (LLMs) are trained on vast datasets of human behavioral text, raising the critical question of whether artificial systems inherit the affective irrationalities of human moral reasoning [cite: 7].

A 2026 empirical investigation into LLMs revealed that these models do, in fact, replicate the identifiable victim effect [cite: 7, 43]. When tasked with allocating resources in simulated humanitarian triage scenarios, LLMs consistently favored narratively described individuals over statistically characterized groups facing equivalent hardship [cite: 7, 43]. 

More concerningly, the standard AI prompting technique known as Chain-of-Thought (CoT)—designed to force models to engage in deliberative, logical reasoning—actually *amplified* the bias. Standard CoT prompting nearly tripled the identifiable victim effect size in the model's output (jumping from $d=0.15$ to $d=0.41$) [cite: 7, 43]. The mechanism behind this amplification is described as an "autoregressive emotional runaway" [cite: 7]. Rather than enforcing dispassionate logical analysis, the instruction to "think step by step" allowed the model's decoder to serially generate emotionally reinforcing justifications that magnified the initial affective response to the identifiable victim's narrative [cite: 7]. The effect could only be collapsed to statistical insignificance by enforcing an explicitly "Utilitarian CoT," which rigidly forced the model to reason strictly about cost-effectiveness and population-level impact [cite: 7, 43]. 

This finding has profound implications for the deployment of AI in humanitarian contexts. If algorithmic systems are used to draft marketing copy or evaluate resource allocation without strict utilitarian guardrails, they may inadvertently exacerbate the unequal distribution of resources, driving donations based on narrative vividness rather than objective, aggregate need.

## Conclusion

The identifiable victim effect remains one of the most influential concepts in the study of charitable giving and cause-related marketing, yet contemporary research has fundamentally altered how it is understood. It can no longer be viewed as an infallible law of human behavior. High-powered replications and field studies from 2023 to 2026 indicate that the effect is highly fragile, susceptible to publication bias, and easily neutralized by competing variables such as out-group prejudice, the donor's baseline mood, and the complexity of real-world giving environments [cite: 10, 16, 20]. 

However, its underlying premise—that human beings are moved to act by emotional resonance and narrative concreteness rather than abstract statistics—remains valid in strategic applications. In cause-related marketing, the deliberate use of visual identifiability consistently outperforms faceless data by bridging psychological distance and allowing donors to perceive their impact as immediate and concrete [cite: 5, 32, 33]. 

Ultimately, the most effective charitable marketing strategies do not rely exclusively on the raw emotional distress of a single victim, as this risks ethical exploitation and compassion fade. Instead, leading organizations deploy a synthesized approach: leveraging the identifiable narrative to trigger the initial affective engagement of System 1 cognition, while simultaneously providing clear, transparent data to satisfy the deliberative requirements of System 2. As technology evolves—with AI systems demonstrating a propensity to inherit and amplify these exact human cognitive biases—maintaining this careful balance between narrative empathy and statistical reality will become increasingly vital for the ethical and efficient allocation of philanthropic resources.

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86. [Happy to help - if it's not too sad: The effect of mood (PLoS ONE)](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0252278)
87. [Creating High-Conversion Nonprofit Impact Reports (FundRobin)](https://www.fundrobin.com/articles/thought-leadership/creating-high-conversion-nonprofit-impact-reports-ai/)
90. [Poverty Porn and the Identifiable Victim Effect (ISAGSQ)](https://academic.oup.com/isagsq/article/1/4/ksab032/6404464)
92. [Huggingface Papers: Victim Attribution (HuggingFace)](https://huggingface.co/papers?q=victim%20attribution)
93. [Increasing Donations Through Appeal Types (Faunalytics)](https://faunalytics.org/increasing-donations-through-appeal-types-exposure-and-donor-characteristics/)
95. [Narrative over Numbers: The Identifiable Victim Effect in LLMs (arXiv)](https://arxiv.org/html/2604.12076v1)
96. [Individual differences in reasoning style as a moderator (Taylor & Francis)](https://www.tandfonline.com/doi/full/10.1080/15534511003707352)
97. [COVID-19 Charity Advertising: Identifiable Victim Message Framing (ResearchGate)](https://www.researchgate.net/publication/352546486_COVID-19_Charity_Advertising_Identifiable_Victim_Message_Framing_Self-Construal_and_Donation_Intent)
98. [Victim identifiability, number of victims, and unit asking in charitable giving (PLoS ONE)](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0300863)
100. [Identifiable Victim Effect (Wikipedia)](https://en.wikipedia.org/wiki/Identifiable_victim_effect)
106. [Thinking good and bad: Deliberative thinking and the singularity effect (Cambridge Core)](https://www.cambridge.org/core/journals/judgment-and-decision-making/article/thinking-good-and-bad-deliberative-thinking-and-the-singularityeffect-in-charitable-giving/FE35E54B8265604DFEE3189149449ECF)

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35. [ignitevisibility.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQH0rqK-hTBX32f-CbwujBBqsceTX3Uto8NSpJxJ92FeMGLOVB_ZLtG7IIuGID8fAi8b-2_sVIVY5tLMqJhaPg6CIkCe99cEl2ldDYinPUlm8oyw2eO5JQyHsWKyq9RGhokXKI1VlMKucSBdPQWX)
36. [mailjet.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFE8Sxt9Uam7e95wl4QEQtt8uAmNbdedQTjQwYG-LaudmNwm8SCKVZDemKOqa0PHi6jzN8o32KopnVMnuo5QK8LRA74R2cmQQRQY-VWpmBcf_KPS4cwLvPhLfM7JY_g-JmfGPGf6a7HkXkBwTcCyd0c2-MdhpxWHfbwC4vi)
37. [lineardesign.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQGVj6ugIuJuj968q_s3yD4tJUN0xg_QnbIjh32OUfX-9siidRKwtYTbgt1cb-TE6_7t6RSTzXJRLXAnM66AYwLUNK6hqoqFwe9rH4qusG7ZyPfx8oXCbDd7Jlh2R8woFVrbY9zuyfInKwTabMu5ITeKcz1c5dfGpoyeXk1dQ3-CguA=)
38. [helpyousponsor.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQGQQil34KRI58ef3AFDm6b1HBfh5Ju1emcduSeKfPFguhYqydwuDSkkk1bL6yu4LnYkV3zjVbiBF-0oCCYIr2VfeFKckFlRFzmgQOj1XHk6L-dSl72ZcBxiKuK9CrvHlxFKRdy7-sNNUcO0cSYpO5YQymFRHA2Wqk2F4b4S1T4oJleBhsA4VizyMVsf6hvhkO04v9u98w==)
39. [callhub.io](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQEG7nY1agqggJGjCip6fUy4nq6bnsrnpjbpn5nuXXDgn4lpWczBYd-EIsUsotKuea8E3GbwXGNYS47s6XtI4cSdiwCi5wdDClvc878Xy3IEEV9gSHiW8yw7eiOyCMDAdwwBLrfNeQmSRn98jqYroA==)
40. [fundrobin.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHM-OUw0cEu07-g7b_nEHeA-jq8_FI3oxqXt8BhzHBolGCRpTyfhZUKOoX9_IMG0G2nrDSN5Ko0arNn-9TBg45EjoIvFg9l-_1wlbFh6bLNTHMBeP9SnrF8cywNJD5kP7k9VMb6WkeH-Y4WaqLkC1s_uLXQhsawJIK2TX9XtQqGk5Rg8P5vgttki2hMisHaoGfrhowanxNbmvFSnGiaWpjNqQ==)
41. [reefdigital.com.au](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQE-B6kcCzGnGjukVq-TgXCQmbczPhdYLss5J8Ep6wqpwkZIPwcp58hExwKIGx-StECL7cZkOXHSRbNISm1j_53iNRLO4oTsyd4_PXPdIjfijuUikNnBVtYf35-7xRVamxwrmMLtSOqdaKFmd5mN8oADLGe10oqO_gDvUtpH4709iTkb3q3WZJE3ynvzNzYKzTY=)
42. [nptechforgood.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQE1rmjM0A8QfkH_M1fXeunmeWCA_97-1NsxptYHMkgxBDFcb5RDjsetdHac9b8xDNVLK9g1KZU1MD7o4_cyeTanixn6uc4RHYmx-5Jr5q_g4eNtBoeR1H3ZlNJJEY1uH1QnZZT7Ag9l8vuxiIgswSeJWBNCIfxAKVIFfOSAlk_Ljpkdkwup5EjFtnpK1wSg5nO2tVBaKCMp24z2LRjvNX6G9-JKxiDSiCDaCMg=)
43. [huggingface.co](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQEN7KOFXXLokmrRqj2JMdeC8JFb0VwwC4PiIjFpXCG3moQKYykZOMPBtnDtqpEWh2XWb22zHgsIGPAWaSgRp5oDiABfvtuWoYnZGmEIe5P0h8bRv54TZkKELlvpZASA31x49DvXF7OyF_Du)