Determinants of the Assimilation of Information Technologies in Human Resource Service Delivery in Canada and the United States of America
Bibliographic record
Abstract
The use of Information Technology (IT) in the delivery of Human Resource (HR) services -a traditionally laborious, paper-intensive operation—is spearheading a revolution in the way personnel services are delivered. Based on a thorough review of practitioner and academic research literatures, this dissertation studies the determinants of assimilation for the following HR Information Technologies (HRITs): (1) HR functional applications; (2) Integrated HR software suites; (3) Interactive (or Automated) Voice Response systems; (4) HR intranets; (5) Employee Self-Service applications; (6) Manager Self-Service applications; (7) HR extranets; and (8) HR portals. The assimilation of HRITs is operationalized through a multidimensional variable, HR Technology Intensity (HRTI), that includes information on the assimilation stage of the technologies used in the firm, as well as on the penetration with which they are being used. Using a Diffusion of Innovations perspective, four sets of factors are hypothesized to influence HRTI: Environmental Factors (more specifically, Environmental Turbulence), Organizational Factors (Top Management Support and Uniqueness of HR Practices), User Department Factors (HR Innovation Climate, HR IT-Absorptive Capacity and HR-Technology Champion), and IS Department Factors (HR IS Resource Availability and HR-IS Relationship). The latter are theorized to mediate the relationship between the User Department factors and HRTI when the Locus of Responsibility for HR-Technology includes at least partially the IS function -a moderated mediation functional form (James & Brett, 1984). Data from 155 HR Executives from firms in Canada and the United States were collected using an Internet-based survey, yielding a response rate of 21.3%. No consequential differences were found among country sub-samples. Hierarchical regression analyses offered support for the hypotheses concerning the relationship between HRTI and Top Management Support (an Organizational Factor), and HR Innovation Climate (a User Department Factor). Moderated mediation analyses also substantiated the hypothesis linking HR Innovation Climate and HRTI by way of HR-IS Relationship when the Locus of Responsibility for HR-Technology includes the IS function. Finally, an alternate dependent variable (the Sum of Percentage Penetration of IT for HR) offers converging support for the analyses linking predictor and independent variables. Implications, limitations of this investigation, and suggestions for future research conclude this dissertation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".