Bibliographic record
Abstract
Purpose Drawing on the HR technology (HRT) and information systems (IS) literatures, this study seeks to identify macro-level factors that influence the performance of HRT systems. A second objective is to assess the relative contribution that HRT goal realization makes to organizational satisfaction with HR services. Design/methodology/approach This investigation draws on a web-based survey of 169 US and Canadian firms targeting HR executives as key informants. Structural equation modeling (SEM) tested the roles that organizational support, capabilities and aspects of the environment play in technology goal attainment and collective satisfaction with HR services. Exploratory factor analysis (EFA) evaluated the properties of several key scales and supported their usage. Moderated regression analysis further assessed whether HRT age influenced certain relationships. Findings As predicted, system goal realization was positively related to the level of support from an HRT champion and an HR innovation climate, while being negatively related to HRT mimetic isomorphism. HR service satisfaction, in turn, was positively related to HRT goal realization, the HR innovation climate and HR environmental munificence. It also was determined that HRT champions had a stronger positive impact on goal realization for younger technology portfolios. This too was expected. Research limitations/implications External validity would be strengthened by not only increasing sample sizes for the USA and Canada, but also targeting more nations for data collection. The model's explanatory power may also be enhanced by improving the measurement of several predictors (e.g. top management support, absorptive capacity), as well incorporating constructs that focus on users (e.g. group potency, collective efficacy). Practical implications These findings underscore the need to proactively screen and structure the surrounding environment to facilitate portfolio success. Greater emphasis must be placed on (1) identifying and empowering HRT champions, (2) fostering an innovation climate in the HR function and (3) conditioning HRT purchases on “mindful” adoption. Doing so should not only increase the prospects of realizing goals, but also elevate satisfaction with HR services. Originality/value This is the first study to formally assess the effects that organizational and environmental context have on overall HRT systems performance. Prior research has focused on linking the local conditions of individual users to their perceptions and usage of HR technologies.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".