An AI-Driven Framework for Organizational E-Reputation Management: Combining Predictive Monitoring, Weak Signal Detection, and Explainable Governance

Introduction:
Organizational e-reputation has become a strategic asset, yet reputational crises often emerge from weak digital signals that conventional monitoring tools detect too late. Existing solutions remain fragmented: sentiment analysis platforms lack decision-support capabilities, while predictive systems rarely address governance or explainability. This work proposes an intelligent e-reputation management framework integrating predictive artificial intelligence with generative AI (large language models) in a governed, explainable architecture.

Methods:
Following a design science research methodology, we identify digital signals and determinants of e-reputation through a systematic literature review and a multi-source data model (social media, press, forums, reviews). The framework combines: (i) a predictive layer for reputation scoring, risk classification, and early alerting; (ii) a generative layer for synthesis, explanation, and scenario-based recommendations; and (iii) a governance layer ensuring traceability and explainability (XAI). Multilingual content (French, Arabic, Darija, English) is handled through a unified architecture: a shared cross-lingual transformer backbone with language identification and lightweight dialect adapters, addressing code-switching and transliteration rather than training independent per-language models.

Results:
As a design-science contribution, we present the conceptual architecture, a taxonomy of digital reputation signals, and a weak-signal detection pipeline connected to decision-support dashboards. A proof-of-concept prototype of the collection and detection layers is under implementation. The evaluation protocol specifies concrete indicators: precision, recall, and F1 for signal classification; detection lead-time and time-to-decision; precision@k for severity-ranked alerts; and expert-rated Likert grids for interpretation quality, using public multilingual corpora and organizational case-study data as benchmarks.

Conclusions:
The framework contributes an integrated, explainable, multilingual approach to AI-assisted e-reputation management. Ongoing work focuses on completing the prototype and empirical validation through organizational case studies.