A mixed-methods roadmap lifts bibliometric standards: combining expert elicitation from 53 senior editors with a systematic review of 195 studies yields practical, reproducible guidance to make literature mappings clearer and more useful — including for fast-moving fields such as AI economics.

Main Finding

The paper demonstrates that a sequential mixed-methods approach—combining thematic, expert-driven narrative synthesis with a systematic review of existing bibliometric studies—can produce evidence-based, practical guidance that raises the clarity, rigor, accessibility, and real-world usefulness of bibliometric analyses. It delivers a step-by-step roadmap to help scholars conduct state-of-the-art bibliometric reviews.

Key Points

• Methodological contribution: Advocates a sequential mixed-methods design that pairs expert elicitation with a systematic appraisal of bibliometric literature.
• Empirical basis: Findings are grounded in input from 53 senior editorial members of leading hospitality and tourism journals and a review of 195 bibliometric studies.
• Output: A structured, actionable guide aimed at improving decisions about bibliometric choices (e.g., scope, techniques, visualization, interpretation).
• Goals: Improve transparency, reproducibility, and theoretical and managerial relevance of bibliometric studies.
• Intended audience: Scholars conducting bibliometric reviews and editors/decision-makers who rely on such syntheses.
• Value-add: Synthesizes cutting-edge practice into accessible recommendations to standardize and elevate bibliometric work.

Data & Methods

• Design: Sequential mixed-methods approach combining qualitative expert input and quantitative/literature synthesis.
• Expert elicitation: Thematic, synthesized narrative commentary based on contributions from 53 senior editorial board members (leading journals in hospitality and tourism).
• Literature review: Systematic consideration of 195 recent bibliometric investigations to identify prevailing practices, strengths, and weaknesses.
• Integration: Cross-validation of themes from expert commentary against patterns observed in the bibliometric literature to produce evidence-based guidance.

Implications for AI Economics

• Better mapping of the field: Applying the paper’s roadmap enables more rigorous, transparent bibliometric mappings of AI economics (e.g., tracking research clusters like algorithmic fairness, automation impacts, market microstructure with AI).
• Method selection and reporting: AI economics bibliometricians should adopt explicit, documented decision rules (database choice, search strings, inclusion/exclusion, disambiguation, normalization) to enhance reproducibility and comparability across studies.
• Expert validation: Complement algorithmic analyses (co-citation, co-word, bibliographic coupling) with domain expert review to ensure topical labels and interpretations are meaningful for fast-evolving AI economics topics.
• Mitigating biases: Use the guide’s recommendations to recognize and mitigate common bibliometric biases relevant to AI economics—database coverage gaps, language and regional skews, and rapid publication cycles in AI-related outlets.
• Policy and practice relevance: Higher-quality bibliometric syntheses can better inform policy makers and industry stakeholders about research trends, evidence gaps, and priority areas (e.g., labor displacement, regulation, productivity effects).
• Reproducibility and data sharing: Encourage pre-registration (where appropriate), sharing of search strategies, datasets, code, and visualization outputs so AI economics bibliometric studies are verifiable and updateable as the literature evolves.
• Tooling and triangulation: Combine multiple bibliometric techniques and (where possible) altmetric indicators to capture both scholarly structure and societal engagement of AI economics research.
• Future research agenda: Use standardized, transparent bibliometric practices to build cumulative meta-knowledge in AI economics—facilitating comparative studies across subfields, temporal trend analyses, and mapping of interdisciplinary linkages (e.g., econ + ML + ethics).

Practical next steps for AI economics researchers: adopt the paper’s step-by-step checklist when designing bibliometric studies; document every methodological choice; validate thematic labels with domain experts; and publish supplementary materials (search strings, raw bibliographic files, code) to enable replication and longitudinal updates.