Customer behavior and adoption · Empirical quantitative survey study
The adoption of artificial intelligence in human resources management practices
Nishad Nawaz, Hemalatha Arunachalam, Barani Kumari Pathi, Vijayakumar Gajender (2024). International Journal of Information Management Data Insights.
Plain-English take
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.
How much weight to give this
3.5/5 Useful evidence with meaningful caveats
The 1–5 evidence-check score is a practical reading aid, not a formal peer review or a probability that a conclusion is true. It combines design strength (2 points), sample reporting (1), comparison/control clarity (1), and how much of the paper could be checked (1). Access affects how confidently this directory can assess a study, not the study's underlying quality. Blinding is reported separately because it is not relevant to every design.
Synthesis or analytical design addresses the question, but conclusions depend on included literature or model assumptions.
The reviewed source reports a sample or corpus size (274); size alone does not establish representativeness.
The comparison structure is limited or depends on the paper's model/context.
Firecrawl retrieved a full paper or full-text publisher page for this annotation.
Double-blind? Blinding was not established from the captured source; it may not apply to this study design.
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.
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.
This is a paraphrase of the source. Check the original paper for definitions, estimates, statistical uncertainty and analysis details.
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.
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.
Evidence access
Full text retrieved from publisher or linked open repository
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.
If only an abstract or metadata was available, the profile is limited to claims supported there. A paper's paywall does not change its evidentiary status, but it limits what we can substantiate from accessible text.