AI-Enabled Participatory Marketing: A Bibliometric Analysis and Research Agenda

Aim/Purpose: This study addresses the structurally skewed measurement of performance outcomes in the rapidly expanding AI-enabled participatory marketing literature. This paper aims to map the intellectual structure of this field, to measure the amount of outcome-related research in six performance domains and to identify the gaps that should guide the next cycle of empirical investment for the field. Background: This fragmentation makes it harder to compare studies, carry out meta-analytic synthesis, and for practitioners to predict what outcome dimensions will be most reliably influenced by investments in AI marketing. Bibliometric analysis offers a reproducible, data-driven tool for unlocking the structural distribution of this research in an expanding, technology-intensive corpus. Methodology: Following a three-stage sequential screening process (criteria: publication date 2015 or later; peer-review status; and substantial treatment of AI marketing or participatory marketing outcomes), a final corpus of 467 records (2015–2026) was constructed. We applied four complementary bibliometric methods: 1. publication trend analysis; 2. source and author analysis; 3. keyword co-occurrence network analysis; and 4. performance outcome mapping protocol with domain-specific lexicons. Contribution: This paper aims to provide the following: (1) what is, to the authors’ knowledge, the first comprehensive bibliometric map of AI-enabled participatory marketing research organized around a six-domain performance outcome framework; (2) a quantitative distribution of outcome coverage revealing overrepresented areas (efficiency: 40.3%; stakeholder trust: 38.5%) and a critical empirical gap (market reach: 8.4%); and (3) a six-direction research agenda based on bibliometric evidence rather than researcher judgment to rebalance the field’s methodological portfolio. Findings: Five thematic clusters were identified (% of 467-paper corpus; multi-assignment permitted): AI and Data Infrastructure (37.3%); Digital Marketing Strategy (33.0%); Stakeholder and Participatory Engagement (58.9%); Organizational Dynamics and Leadership (30.0%); and Performance and Competitive Outcomes (60.0%). The corpus grew at a CAGR of 23.3% (2015-2024), with a YoY growth of 192.6% in 2024. Recommendations for Practitioners: AI marketing managers should prioritize the following: (1) longitudinal measurement frameworks that track ROI and competitive advantage over 3–5 year cycles; (2) GIS-enabled and cross-national market reach metrics as primary KPIs, not secondary correlates; (3) context-adapted AI deployment models for emerging-market settings; (4) systematic adverse-outcome monitoring protocols (privacy, algorithmic bias, stakeholder exclusion); (5) an integrated AI Marketing Performance Index (AMPI) encompassing all six outcome domains; and (6) multi-group analysis of industry and firm-size boundary conditions before scaling AI marketing investment. Recommendation for Researchers: Priorities are (1) longitudinal panel designs that track competitive advantage over a 3-5 year period; (2) market reach as the primary dependent variable using GIS and cross-national instruments; (3) studies of the emerging market context with adapted measurement instruments; (4) negative outcome and failure-mode studies; (5) an integrated AI Marketing Performance Index; and (6) moderator studies using multi-group structural equation modelling. Impact on Society: As AI marketing systems scale globally, their structural bias toward documenting best-case performance scenarios while ignoring adverse outcomes – privacy breaches, algorithmic bias, stakeholder exclusion – has direct societal consequences. The research agenda proposed here seeks to produce corrective evidence that supports equitable and transparent AI governance in marketing contexts. For the informing science and knowledge management community, these findings underscore the urgency of developing structured, multi-domain performance frameworks that can distinguish value creation from value extraction in AI-intensive environments. Future Research: Six directions are proposed. Most urgent are studies of longitudinal competitive advantage, market reach as primary outcome, and systematic mapping of negative/paradoxical outcomes. Medium term priorities are integrated performance index and cross national emerging market studies. With the corpus going beyond 250 annual publications, computational text analysis and co-citation network analysis should be based on this bibliometric base.