El Reto del Marketing Social ante la Inteligencia Artificial: un análisis comparado en distintos contextos geográficos y sistemas políticos
Open accessAbstract
La irrupción de la inteligencia artificial (IA) en las estrategias de comunicación persuasiva ha transformado radicalmente el marketing social, entendido como el uso de técnicas de mercadotecnia para promover cambios de comportamiento en beneficio del bienestar individual y colectivo. El presente artículo examina los retos éticos, operativos y regulatorios que enfrenta el marketing social ante la adopción de sistemas de IA generativa, analítica predictiva y automatización algorítmica. Mediante un método comparativo de casos múltiples, se analizan cuatro contextos geográficos y sistemas políticos contrastantes —la Unión Europea, los Estados Unidos, la República Popular China y América Latina— con el objetivo de identificar patrones comunes y divergencias en la manera en que los marcos regulatorios, las tradiciones culturales y los modelos de gobernanza condicionan el uso de la IA en campañas de bien social. Los resultados muestran que, pese a las diferencias normativas, existen tensiones recurrentes en torno a la privacidad de datos, el sesgo algorítmico, la manipulación conductual y la brecha de capacidades institucionales, mientras que las divergencias se concentran en el grado de centralización regulatoria y en la prioridad otorgada a la innovación frente a la protección de derechos. Se concluye que el marketing social requiere marcos éticos transnacionales y capacidades técnicas locales para aprovechar el potencial de la IA sin comprometer la confianza pública ni la autonomía de los públicos objetivo.
AI snapshot
Written from the paper’s full text.
TL;DR
This comparative qualitative study examines how social marketing's encounter with artificial intelligence plays out across four political/geographic contexts (European Union, United States, China, Latin America), arguing that the challenge is not merely technological but embedded in each region's legal, political and cultural traditions. It identifies three cross-cutting challenges alongside divergences traceable to political systems and institutional capacity.
Key findings
- Three convergent patterns appear in all four cases regardless of political system: a structural tension between AI's persuasive effectiveness and risks to audience autonomy and privacy; a capacity gap between organizations with advanced technical resources and those without (typically NGOs and underfunded public health programs); and rising public distrust of AI-generated or AI-curated content, which erodes the credibility social marketing depends on.
- Regulatory divergence is attributed to political system: the EU codifies AI risks in a single horizontal, risk-based instrument; the US delegates to sectoral agencies and state jurisprudence; China integrates AI regulation into state governance of public information.
- The stated ultimate purpose of regulation also differs — individual rights protection (EU), market competition (US), and maintenance of sociotechnical order (China).
- Latin America's central divergence is described as one of capacities rather than regulation: the region often adopts standards designed elsewhere but faces structural limits in financing, connectivity and technical training.
Methods
- Qualitative comparative design following a "most different systems" logic, holding the unit of analysis constant (ethical, operational and regulatory challenges of AI-enabled social marketing) while varying geographic and political context.
- Four cases selected for maximum variation: EU (supranational horizontal risk-based regulation), US (decentralized sectoral self-regulation and market competition), China (centralized state control oriented to sociotechnical infrastructure), and Latin America as a regional bloc (emerging frameworks dependent on multilateral bodies).
- Four common analytical categories applied per case: AI regulatory framework; dominant ethical approach; characteristic AI use in social marketing campaigns; and principal challenge identified in the literature. Procedure ran in three phases — documentary evidence collection from academic and international-organization sources published 2021–2026, coding into a comparative matrix, then horizontal (by category across cases) and vertical (within each case) comparison.
Limitations
- Stated by the paper: the comparison rests on secondary documentary evidence and includes no primary data from specific social marketing campaigns, which prevents establishing causal links between regulatory frameworks and actual campaign outcomes; the authors propose future primary case studies and quantitative methods measuring differential AI impact on behavior-change indicators.
- Evident from the text: Latin America is treated as a single regional bloc despite acknowledged internal heterogeneity, and no measurable campaign outcomes or effect sizes are reported anywhere in the analysis.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
10 of 15 cited sources checked · 0 supported · 0 flagged · accuracy not yet measured
- Premisecitation
The absence of a common regulatory architecture makes it difficult to standardize best practices for responsible AI use for persuasive purposes, including social marketing.
“Al-Maamari (2025) profundiza en esta comparación al analizar los marcos de gestión de riesgo de la IA en la Unión Europea, Estados Unidos, el Reino Unido y China, evidenciando que la ausencia de una arquitectura regulatoria común dificulta la estandarización de buenas prácticas en el uso responsable de la IA con fines persuasivos, incluido el marketing social.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Al-Maamari (2025) source
The Introduction (p. 2) compares distinct AI governance approaches in the EU, U.S., UK, and China, and §6.2 (p. 17) says their divergent priorities make full harmonization of AI standards unlikely in the near future. The paper does not extend that finding to responsible AI use for persuasion or social marketing, so the application-specific part of the claim is unsupported. (LLM, conf=0.87)
Experimental — accuracy not yet measured
- Premisecitation
In social marketing, AI can enable more effective behavior-change interventions tailored to each public's cultural particularities.
“Aplicado al terreno del marketing social, este potencial se traduce en la posibilidad de diseñar intervenciones de cambio de comportamiento más eficaces y ajustadas a las particularidades culturales de cada público (Evans et al., 2024;Beyari & Hashem, 2025).”
Quote in the paperNot a substantial claim2 of 2 sources checked- cites Evans et al., 2024 source
Section 2.3 says AI can tailor social-marketing content to audience segments and make it more likely to promote behavioral change. The paper does not specifically say that interventions can be adjusted to each public’s cultural particularities; its examples focus on preferences, lifestyle, and behavior. (LLM, conf=0.84)
Experimental — accuracy not yet measured - cites Beyari & Hashem, 2025 source
The Literature Review (§2.3; page unavailable) says AI enables personalized marketing messages for diverse consumer groups, supporting the tailoring aspect. It does not address social-marketing behavior-change interventions. The Conclusions (§8; page unavailable) identify differences across cultures as a topic for future research, rather than establishing that AI can tailor interventions to each public’s cultural particularities. (LLM, conf=0.86)
Experimental — accuracy not yet measured
- Interpretationcitation
The challenge of social marketing before AI cannot be reduced to a merely technological question; it is a phenomenon deeply rooted in the legal, political, and cultural traditions of each region.
“Los hallazgos de este análisis comparativo sugieren que el reto del marketing social ante la inteligencia artificial no puede reducirse a una cuestión meramente tecnológica, sino que constituye un fenómeno profundamente enraizado en las tradiciones jurídicas, políticas y culturales de cada región.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Chun et al., 2024 source
The paper’s Abstract (p. 1) says that EU, Chinese, and US approaches to AI regulation reflect distinct cultural and political perspectives, supporting the regional context of the claim. It analyzes AI governance, however, not the challenge of social marketing in particular; that application is not established by the cited paper. (LLM, conf=0.84)
Experimental — accuracy not yet measured
- Findingcitation
In all cases, growing public distrust of AI-generated or curated content erodes the credibility capital that social marketing needs to change behaviors.
“En tercer lugar, en todos los casos se observa una creciente desconfianza pública hacia los contenidos generados o curados por IA, lo cual erosiona precisamente el capital de credibilidad que el marketing social requiere para modificar comportamientos (Kaur et al., 2025).”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Kaur et al., 2025 source
The Introduction says AI-generated content raises concerns about authenticity and trustworthiness, and that manipulated content can undermine public confidence. It does not establish growing public distrust in all cases or link that distrust to social marketing’s ability to change behavior. The copy gives no page number for this passage. (LLM, conf=0.86)
Experimental — accuracy not yet measured
- Rests on an assumption: Social marketing requires credibility capital to modify behaviors.
- Findingcitation
The comparative analysis found a common pattern across all four cases: a structural tension between AI's persuasive efficacy and the risk of violating target publics' autonomy and privacy.
“En primer lugar, todas las regiones enfrentan una tensión estructural entre la eficacia persuasiva que ofrece la IA y el riesgo de vulnerar la autonomía y la privacidad de los públicos objetivo, lo que confirma observaciones previas sobre el sesgo algorítmico en modelos de marketing (Akter et al., 2022).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Akter et al., 2022 source
- Findingnone given
In Latin America, the central divergence is not regulatory but capacity-based: the region often adopts standards designed elsewhere but faces structural limitations in financing, connectivity, and technical training that condition effective implementation.
“Finalmente, en el caso latinoamericano la divergencia central no es de naturaleza regulatoria sino de capacidades: la región adopta con frecuencia estándares diseñados en otros contextos, pero enfrenta limitaciones estructurales de financiamiento, conectividad y formación técnica que condicionan su implementación efectiva.”
Quote in the paperSubstantial claim - Findingcitation
In all four contexts, there is a gap between social marketing organizations with advanced technical capabilities and those lacking them, usually NGOs or public health programs with limited funding.
“En segundo lugar, en los cuatro contextos se documenta una brecha entre las organizaciones de marketing social con acceso a capacidades técnicas avanzadas y aquellas -generalmente organizaciones no gubernamentales o programas de salud pública con financiamiento limitado-que carecen de dichas capacidades, replicando a escala institucional la brecha digital ya señalada en la literatura sobre IA y de”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Organización Panamericana de la Salud, 2024
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
Algorithmic bias can distort audience selection and perpetuate exclusion of vulnerable groups that social marketing aims to benefit.
“El sesgo algorítmico puede distorsionar la selección de audiencias y perpetuar la exclusión de grupos vulnerables, precisamente aquellos que el marketing social busca beneficiar (Akter et al., 2022).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Akter et al., 2022 source
- May combine two claims. 8
May combine two claims.
PremisecitationRecent literature reports an accelerated growth of research on AI in marketing since 2021, driven by generative language models and low-cost predictive analytics.
“La literatura reciente coincide en señalar una expansión acelerada de la investigación sobre IA aplicada al marketing a partir de 2021, impulsada por la disponibilidad de modelos de lenguaje generativo y de analítica predictiva de bajo costo (Rita et al., 2025).”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Rita et al., 2025 source
Section 2.1 (page unavailable) reports a sharp rise in AI-and-marketing publications: 1,493 articles appeared in 2019–2022, compared with 2,255 since 1980. It does not locate the start of that growth specifically in 2021. Section 3.1 discusses generative models and predictive applications, but does not say that their availability, or low-cost predictive analytics, drove the growth in research. (LLM, conf=0.87)
Experimental — accuracy not yet measured
May combine two claims.
PremisecitationThe EU, China, and US have developed regulatory approaches reflecting distinct cultural, political, and economic values: the EU prioritizes fundamental rights through a tiered risk framework, the US relies on sectoral self-regulation and market competition, and China treats AI as sociotechnical infrastructure under state control.
“mientras la UE prioriza la protección de derechos fundamentales mediante un marco de riesgo escalonado, Estados Unidos confía en la autorregulación sectorial y en la competencia de mercado, y China concibe la IA como infraestructura sociotécnica sujeta al control estatal.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Chun et al., 2024 source
Split into 3 atoms — 2 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
May combine two claims.
PremisecitationGrowing public distrust of AI-generated content and concerns about personal data use are barriers to the legitimacy of social-good campaigns.
“Asimismo, la creciente desconfianza del público hacia los contenidos generados por IA y las preocupaciones sobre el uso de datos personales representan una barrera para la legitimidad de las campañas de bien social (Kaur et al., 2025).”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Kaur et al., 2025 source
The Introduction identifies personal-data privacy as an ethical concern and says AI-generated content raises concerns about authenticity and trustworthiness that can undermine public confidence. It discusses AI-based social media marketing generally, however, not growing distrust or barriers to the legitimacy of social-good campaigns specifically. (LLM, conf=0.86)
Experimental — accuracy not yet measured
May combine two claims.
FindingcitationA second divergence concerns the ultimate purpose attributed to regulation: protection of individual rights in the EU, preservation of market competition in the US, and maintenance of sociotechnical order in China.
“Una segunda divergencia concierne a la finalidad última atribuida a la regulación: protección de derechos individuales en la Unión Europea, preservación de la competencia de mercado en Estados Unidos, y mantenimiento del orden sociotécnico en China.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Chun et al., 2024 source
Section 4.1 (p. 3) describes a fundamental-rights agenda in the EU AI Act, and section 5.1 (p. 9) says China emphasizes social harmony. Section 6.3 (p. 16) says the US approach promotes innovation through competition, but does not identify preservation of market competition as its ultimate purpose. The paper also describes multiple aims in each jurisdiction, so the claim's three-way account of their ultimate purposes is too categorical. (LLM, conf=0.86)
Experimental — accuracy not yet measured
May combine two claims.
FindingcitationThe main regulatory divergence is the degree of centralization: the EU codified AI risks in a single legal instrument, the US delegated regulation to sectoral agencies and state jurisprudence, and China integrated AI regulation into state governance of public information.
“La principal divergencia se refiere al grado de centralización regulatoria: mientras la Unión Europea ha optado por codificar exhaustivamente los riesgos de la IA en un instrumento jurídico único, Estados Unidos ha preferido delegar la regulación en agencias sectoriales y en la jurisprudencia estatal, y China ha integrado la regulación de la IA dentro de una lógica de gobernanza estatal de la”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Chun et al., 2024 source
The AI Governance section (p. 3) contrasts the EU’s centralized, risk-based AI Act with a decentralized US approach, and the Conclusion (p. 26) describes US oversight delegated to federal agencies. But the paper discusses state regulations rather than state jurisprudence; it also characterizes China’s system as a hybrid that includes decentralized local innovation (5.1 Overview, p. 9), not simply as state governance of public information. (LLM, conf=0.85)
Experimental — accuracy not yet measured
May combine two claims.
PremisecitationAI technologies allow personalizing messages, automating audience segmentation, and predicting consumer behavior with previously unattainable precision.
“Estas tecnologías permiten personalizar mensajes, automatizar la segmentación de audiencias y anticipar el comportamiento del consumidor con niveles de precisión antes inalcanzables (Basu et al., 2024).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Basu et al., 2024 source
May combine two claims.
PremisecitationPAHO has noted AI's transformative potential for public health equity but also highlighted gaps in infrastructure, technical training, and regulatory framework that condition its adoption in Latin America.
“En América Latina, la Organización Panamericana de la Salud (2024) ha señalado el potencial transformador de la IA para la equidad en salud pública, pero también ha subrayado las brechas de infraestructura, capacitación técnica y marco normativo que condicionan su adopción en la región.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Organización Panamericana de la Salud (2024)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
Study design and conductown dataThe study's comparative method relied on secondary documentary evidence and did not include primary data from specific social marketing campaigns, which limits the ability to establish causal relations between regulatory framework and effective campaign outcomes.
“Entre las limitaciones de este estudio cabe señalar que el método comparativo aplicado se basó en evidencia documental secundaria y no incluyó datos primarios de campañas específicas de marketing social, lo que restringe la posibilidad de establecer relaciones causales entre el marco regulatorio y los resultados efectivos de las campañas.”
Quote in the paperNot a substantial claim
The paper relies on this without support.
Social marketing requires credibility capital to modify behaviors.
“el capital de credibilidad que el marketing social requiere para modificar comportamientos”
Cites
The publisher hasn’t deposited this paper’s reference list.
Research works printed but not linked: 1
- Brazilian Journal of Business, Curitiba, v. 8, n. 3, p. 01-11, 2026 jan. 2021
- Authors
- García, N. D. R., & Fernández, A. M. D. E.
- Affiliations
- Consejo de Ciencia y Tecnología del Estado de Tabasco (MX)
- Venue
- Brazilian Journal of Business
- Publisher
- Brazilian Journals
- Published
- 2026-09-24
- Volume
- 8
- Issue
- 3
- Pages
- e89441
- ISSN
- 2596-1934 (ISSN-L)
- OpenAlex
- W7215841902
- Semantic Scholar
- e091f353e68c9f16aa0c5b52dcc734e60d3dbb63
- Cited by
- 0
- References
- 0
- FWCI
- 0.00
- Type
- journal article
- Open access
- diamond
- doi.org
- Locations
- Brazilian Journal of Business (publishedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Social Sciences › Decision Sciences › Information Systems and Management › Technology's Impact on Media
- Keywords
- social marketing, artificial intelligence, generative AI, persuasive communication, AI regulation, data privacy, algorithmic bias, controlling behavior
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