Information technology-enabled explorative learning and competitive performance in industrial service SMEs: a configurational analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose As purveyors of knowledge-based and high value-added services to the manufacturing sector, industrial service small- and medium-sized enterprises (SMEs) must develop the information technology (IT) capabilities that, in combination with other non-IT capabilities, enable their capacity for organizational learning (OL) and for explorative learning in particular. In this context, this study aims to identify the different causal configurations that account for the nonlinear complex interplay of IT capabilities for exploration and strategic capabilities for explorative learning as they affect these firms’ competitive performance. Design/methodology/approach Survey data obtained from 92 industrial service SMEs were analyzed with a configurational approach, using fuzzy set qualitative comparative analysis (fsQCA). Findings As it allows for equifinality, the fsQCA analysis identified two sets of causal configurations that characterize the sampled firms’ explorative learning capability as it relates to competitive performance. In the first set, two configurations were equally associated with high innovation performance, whereas in the second set, four configurations were equally associated with high productivity. Originality/value By viewing explorative learning as a dynamic capability that is enabled by the firm’s IT and strategic capabilities, the study contributes to OL theory by providing a more concrete or “operational” grounding, which allows for a greater practical applicability of this theory. By taking both the configurational and capability-based views of the OL-IT-performance causal framework, the authors provide an empirical basis for unraveling, explaining and understanding the complex non-linear relationships embedded within this framework.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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