Aug 2026· Journal of business and management studies· Vol 8, pp. 97-114· 0 citations
TL;DR
An AI-enabled lifecycle of Creation, Transformation, Transmission, Evaluation, Evaluation, and Governance is proposed and an AI-eWOM fit perspective is developed and a TCCM-organized research agenda identifies priorities for future research.
Abstract
Artificial intelligence (AI) is changing electronic word-of-mouth by participating directly in the production, revision, circulation, evaluation, and governance of consumer information. This systematic literature review synthesizes 71 peer-reviewed studies on how AI functions within electronic word-of-mouth (eWOM), how consumers respond to those roles, and when those effects vary. Using SPAR-4-SLR and PRISMA 2020 procedures, the review draws on database, supplemental, and citation-based searches. It identifies five recurring AI roles -- Creator, Co-Creator, Curator, Intermediary, and Regulator -- and shows that their effects depend largely on perceptions of authenticity, credibility, usefulness, humanness, transparency, and manipulation risk. These effects are context dependent, varying across products, tasks, message formats, consumer orientations toward AI, levels of AI involvement, and platform environments. The review also argues that AI reshapes not only how eWOM is presented, but also what consumers are likely to contribute afterward. Building on these findings, the review proposes an AI-enabled lifecycle of Creation, Transformation, Transmission, Evaluation, and Governance and develops an AI-eWOM fit perspective. A TCCM-organized research agenda identifies priorities for future research.
In a short period, generative artificial intelligence (GenAI) moved beyond specialist settings and entered everyday practices of study, communication, management, and knowledge production. The shift created opportunities for productivity and personalization, but it also brought difficult questions concerning reliability, data protection, copyright, and information integrity. This article examines GenAI applications, limits, and governance requirements, with particular attention to Brazil. The study combines a non-exhaustive integrative review with documentary analysis. Scientific databases and institutional sources were consulted between May and July 2026; after deduplication and screening, 35 documents formed the corpus. A clear pattern emerged. Benefits were more consistent when tasks were well defined, acceptance criteria were explicit, and human oversight was meaningful. Their magnitude varied with data quality, task design, and user experience. Risks involving hallucinations, bias, anthropomorphism, misinformation, opacity, cognitive dependence, and environmental costs remain. As an applied contribution, the article proposes the GERA-BR Framework, structured around seven connected movements: govern, frame, safeguard data, assess, conduct human review, trace, and learn. Responsible adoption, the study concludes, requires evidence, transparency, rights protection, capacity building, and continuous monitoring. As complementary implementation instruments, the study presents maturity levels, an institutional roadmap, and indicators for monitoring quality, risk, and learning.
Maria das Graças da Silva Souza, Klinger Arcanjo Dias, Stefano Eduardo Souza Bogo et al.· Revista de Estudos Interdisc...· 0 citations
It is argued that psychological competence should become a core consideration for model providers, deploying organizations, researchers, and regulators concerned with the real-world effects of human-facing AI systems.
M. Economides, Paul M. Sacher, Samuel Salzer et al.· 0 citations
GenAI's current business value is concentrated in augmenting, rather than automating, decision-making, with the strongest evidence for productivity gains among relatively lower-skilled or lower-performing decision-makers, and the mapping of AI capability boundaries within specific decision domains as the central future research prospect.
Rajidi Rammohan Reddy, Vinodray Thumar, Amar Jyoti Borah et al.· International journal of com...· 0 citations
While artificial intelligence (AI) is increasingly being adopted to mitigate social isolation, existing research has predominantly examined human–AI (H–AI) companionship, potentially overlooking the risk that AI agents are being used as a substitute for authentic human-to-human (H–H) social connections. This scoping review investigates the relatively underexplored domain of AI as a catalytic mediator designed to promote H–H social connections. Guided by PRISMA (Preferred Reporting Items for Systematic Reviews and Meta‑Analyses) and study selection process, a total of 225 studies published between January 2021 and October 2025 were initially retrieved, of which five were ultimately selected for analysis. By examining empirical studies on AI-mediated H–H social connections, this study explores the current state of research, identifies design implications, and then develops preliminary design guidelines for enhancing the quality of H–H social connections. The findings reveal that the reviewed studies frequently exhibit structural limitations, such as a reliance on dyadic relationships, insufficient reciprocal support mechanisms for vulnerable populations, and a lack of exit strategies. Ultimately, this review argues for expanding the social role of AI from that of a companion to that of a catalytic mediator so that AI can support more diverse H–H social relationships.
S. Chung· The International Journal of...· 0 citations
A framework for sustainable, human-centered integration of AI is proposed in which AI is restricted to technical verification and efficiency, while judgments on scientific merit, ethics, and paradigm-shifting research are reserved for appropriately valued human experts.
Artificial Intelligence (AI) often suffers from a "science-to-service gap," where high-performing models fail to translate into effective real-world decision-making. This systematic literature review investigates this divide, identifying three critical barriers: inadequate technical reasoning, organizational resistance, and stringent regulatory compliance. To bridge this gap, we propose a holistic analytical framework anchored in three interconnected pillars: the human–AI relationship, predicated on mutual trust and complementarity; organizational preparedness, necessitating comprehensive cultural transformation and workforce reskilling; and ethical regulation, prioritizing process transparency and robust accountability. Our findings reveal that successful AI integration extends beyond technical optimization, requiring cross-disciplinary strategies such as participative design and collaborative human–AI audits. By synthesizing these dimensions, this study provides a strategic roadmap for enterprises to navigate systemic challenges, fostering a transition from theoretical AI potential to actionable, empowered, and human-centric decision-making systems in complex operational environments. Research indicates that proper application of MCDM techniques can relate AI outputs to real-life decision-making by structuring, enforcing transparency, and justifying AI-scoring results for use within an MCDA (Multi-Criteria Decision Analysis) framework, as demonstrated in complex use cases such as transportation planning.
Karzan Ismael, Ali Mohammed Salih, Zryan Najat Rashid· Knowledge and Decision Syste...· 0 citations
