The complementarity of IT and HRM capabilities for competitive performance: a configurational analysis of manufacturing and industrial service SMEs
Why this work is in the frame
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Bibliographic record
Abstract
Building on the resource-based view (RBV) perspective, we analyse the combined effects of two highly-valued organizational resources, namely information technology (IT) capabilities and human resource management (HRM) capabilities, on the competitive performance of small and medium-sized enterprises (SMEs). Three resource configurations are derived from data on 227 SMEs (121 from the manufacturing sector and 106 from the industrial services sector) through a cluster analysis. These resource configurations are labelled IT Capabilities-dominant Configuration (ITC), e-Business Capabilities-dominant Configuration (e-BC), and HRM Capabilities-dominant Configuration (HRC). This last configuration is the best-performing, followed by the e-BC, with the ITC as the worst-performing. The results also show that manufacturing and service firms are very unevenly distributed within HRC and ITC configurations, suggesting notable differences between the two sectors regarding their respective IT and non-IT capability-building. The fact that service SMEs are overwhelmingly represented (93%) in the worst-performing configuration and completely absent (0%) in the most effective configuration while displaying the strongest IT infrastructure capabilities confirms that the IT productivity paradox is aggravated in service SMEs and calls for further research on this issue.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it