Open Access

Evidence-based Nomenclature and Taxonomy of Research Impact Indicators


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Purpose

This study aims to classify research impact indicators based on their characteristics and scope. A concept of evidence-based nomenclature of research impact (RI) indicator has been introduced for generalization and transformation of scope.

Design/methodology/approch

Literature was collected related to the research impact assessment. It was categorized in conceptual and applied case studies. One hundred and nineteen indicators were selected to prepare classification and nomenclature. The nomenclature was developed based on the principle—“every indicator is a contextual-function to explain the impact”. Every indicator was disintegrated into three parts, i.e. Function, Domain, and Target Areas.

Findings

The main functions of research impact indicators express improvement (63%), recognition (23%), and creation/development (14%). The focus of research impact indicators in literature is more towards the academic domain (59%) whereas the environment/sustainability domain is least considered (4%). As a result, research impact related to the research aspects is felt the most (29%). Other target areas include system and services, methods and procedures, networking, planning, policy development, economic aspects and commercialisation, etc.

Research limitations

This research applied to 119 research impact indicators. However, the inclusion of additional indicators may change the result.

Practical implications

The plausible effect of nomenclature is a better organization of indicators with appropriate tags of functions, domains, and target areas. This approach also provides a framework of indicator generalization and transformation. Therefore, similar indicators can be applied in other fields and target areas with modifications.

Originality/value

The development of nomenclature for research impact indicators is a novel approach in scientometrics. It is developed on the same line as presented in other scientific disciplines, where fundamental objects need to classify on common standards such as biology and chemistry.

eISSN:
2543-683X
Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Computer Sciences, Information Technology, Project Management, Databases and Data Mining