The diffusion of human‐resource information‐technology innovations in US and non‐US firms
Bibliographic record
Abstract
Purpose The purpose of this paper is to evaluate the diffusion patterns of eight information technologies that are transforming HR service‐delivery in North America and Europe: HR functional applications, integrated HR suites, IVR systems, HR intranets, employee and manager self‐service applications, HR extranets, and HR portals. Little is known about the diffusion process for these innovations within or across countries despite mounting professional and academic interest in their proliferation. Design/methodology/approach The paper shows that external‐, internal‐, and mixed‐influence models were applied to the HRIT‐adoption decisions of a cross‐sectional sample of US, Canadian, UK and Irish firms. Parameter estimation was guided by nonlinear regression procedures with starting values for p and q set at levels similar to those reported in prior IT‐diffusion studies. Senior HR executives provided the underlying data by means of a dynamically branching, web‐based survey. Findings The paper finds that overall diffusion was best characterized as an outgrowth of internal influences, fueled primarily by contacts among members in the social system of potential adopters. Similar results were obtained when controls were introduced for national setting, targeted end‐user, and technology type. Research limitations/implications The paper shows that future investigations would benefit from higher response rates outside of North America and the utilization of smaller time intervals to identify when each application was acquired. Practical implications In the paper the modest correlation between the number of acquired ITs and HR‐transaction automation supports the general call for more formalized HR‐technology strategies at the firm‐level to coordinate purchasing and implementation decisions. Originality/value In addition to reviewing the extant literature on HR information systems, this paper presents the first empirical study of the diffusion process for HR software applications within and across countries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".