Measuring the global scientific literature on big data analytics in the LIS: the blending approach of altmetrics and bibliometrics

Abstract

Purpose

This study aims to measure scientific literature on the emerging research area of “big data” in the field of “library and information science” (LIS).

Design/methodology/approach

This study used the “bibliometric method” for data curation. Web of Science and altmetric.com were used. Data analysis and visualisation were done using three widely used powerful data analytics software, R-bibliometrix, VOSviewer and Statistical Package for Social Sciences.

Findings

This study revealed the most preferred venues for publication. Furthermore, this study highlighted an association between the Mendeley readers of publications and citations. Furthermore, it was evident that the overall altimetric attention score (AAS) does not influence the citation score of publications. Other fascinating findings were moderate collaboration patterns overall. Furthermore, the study highlighted that big data (BD) research output and scientific influence in the LIS sector are continually increasing.

Practical implications

Findings related to BD analytics in LIS techniques can serve as helpful information for researchers, practitioners and policymakers.

Originality/value

This study contributes to the current knowledge accumulation by its unique manner of blending the two approaches, bibliometrics and altmetrics.

Citation

Wani, J.A., Rehman, I.U., Ganaie, S.A. and Maqbool, A. (2024), "Measuring the global scientific literature on big data analytics in the LIS: the blending approach of altmetrics and bibliometrics", Information Discovery and Delivery, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IDD-01-2024-0001