AI · Empirical quantitative survey study
The adoption of artificial intelligence in human resources management practices
International Journal of Information Management Data Insights · 2024
Summary
Accuracy, computing power/capacity, and personalization significantly influence time-saving and cost reduction; automation and real-time experience do not significantly influence that outcome. For a founder, use this as a question to test in your own context, not a universal playbook.
For your marketing
Possible founder application
Treat this as evidence for the specific customer behavior and adoption question studied. Compare the setting and sample to your company before changing a growth decision; use a local experiment where the paper does not match your context.
Reported result
What the study found
Accuracy, computing power/capacity, and personalization significantly influence time-saving and cost reduction; automation and real-time experience do not significantly influence that outcome.
Research question
What the authors investigated
Which AI-technology characteristics—accuracy, automation, computing power/capacity, real-time experience, and personalization—affect time-saving and cost reduction in HRM practice adoption?
Design
Online questionnaire analyzed with IBM SPSS 21 and AMOS 21; the publisher abstract describes a proposed research framework but does not give the precise statistical model or scale-development procedure.
Sample and setting
274 IT employees in Chennai City; online questionnaire. The abstract does not state organizational sectors, sampling method, demographics, or whether respondents were HR users.
Scope and transfer
Limitations to keep in view
The publisher abstract does not report limitations, response rate, common-method controls, or generalizability analysis; these should be checked in the full paper rather than inferred.
Research sources
Elsevier publisher page is an open-access record for the 2024 issue article and directly supplies authors, DOI, n=274, Chennai setting, analysis software, and the significant/non-significant findings. Evidence is strong for the reported sample and directional results, but the abstract alone does not establish causal adoption effects. Correction notices were not separately verified in this collection; consult the current publisher record before applying the findings.