2026-10-06T20:35:26Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34054428 2026-10-02T05:35:38Z category_173 portal_316 item_type_3 month_year_10_2026

Table 1_Contemporary AI attitude scale research: a systematic mapping review and bibliometric analysis.xlsx Yusuf Ziya Olpak (25162458) Applied Psychology artificial intelligence attitudes toward AI bibliometric analysis measurement instruments systematic mapping review Introduction<p>The rapid expansion of artificial intelligence (AI) has been accompanied by growing efforts to measure individuals' attitudes toward these technologies. However, existing AI attitude scales vary considerably in their theoretical foundations, dimensional structures, target populations, and application contexts, raising questions about the extent to which they represent equivalent conceptualizations of the construct.</p>Methods<p>This systematic mapping review examined contemporary AI attitude scale research and mapped its thematic, intellectual, methodological, and conceptual structure. A predefined Web of Science Core Collection search identified 298 eligible AI-related psychometric scale studies published between 2022 and June 2026; within this corpus, AI attitude was the most frequently represented construct, with 51 studies forming the final analytic sample. The review integrated descriptive and conceptual synthesis with bibliometric mapping analyses, including author keyword and abstract-term co-occurrence, co-citation, and bibliographic coupling.</p>Results<p>The findings showed that scale development and psychometric evaluation constitute prominent methodological themes in the literature. Co-citation analysis linked the broader technology-acceptance tradition with AI-specific approaches to attitude measurement, while bibliographic coupling revealed four interconnected contemporary research fronts involving scale refinement and contextual extension, education and generative AI, population- and domain-specific measurement, and cross-cultural validation around established scale families. Despite these overlapping knowledge bases, substantial conceptual heterogeneity was evident across instruments, including unidimensional, positive-negative, cognitive-affective-behavioral, functional, ethical, and domain-specific multidimensional conceptualizations.</p>Discussion<p>Overall, the findings indicate that bibliographic and methodological commonality has not produced conceptual uniformity in AI attitude measurement. Future research should therefore prioritize explicit construct definition, conceptual fit between instruments and research purposes, measurement invariance, cross-cultural validation, and cumulative evaluation of established measures rather than assuming equivalence across scales carrying the same construct label.</p> 2026-10-02T05:35:38Z Dataset Dataset 10.3389/fpsyg.2026.1956889.s001 https://figshare.com/articles/dataset/Table_1_Contemporary_AI_attitude_scale_research_a_systematic_mapping_review_and_bibliometric_analysis_xlsx/34054428 CC BY 4.0