Understanding research productivity in the realm of evaluative scientometrics

Abstract

The combination of a variety of inputs (both tangible and intangible) enables the numerous outputs in varying degrees to realize the research productivity. To select appropriate metrics and translate into the practical situation through empirical design is a cumbersome task. A single indicator cannot work well in different situations, but selecting the 'most suitable' one from dozens of indicators is very confusing. Nevertheless, establishing benchmarks in research evaluation and implementing all-factor productivity is almost impossible. Understanding research productivity is, therefore, a quintessential need for performance evaluations in the realm of evaluative scientometrics. Many enterprises evaluate the research performance with little understanding of the dynamics of research and its counterparts. Evaluative scientometrics endorses the measures that emerge during the decision-making process through relevant metrics and indicators expressing the organizational dynamics. Evaluation processes governed by counting, weighting, normalizing, and then comparing seem trustworthy.