Strategic Information Systems Enabling Strategy-as-Practice and Corporate Performance: Empirical Evidence from PLS-PM, FIMIX-PLS and fsQCA
Bibliographic record
Abstract
Many studies have been investigating how IS (information systems) can help build a corporate performance, but there are less research investigating how IS contributing to performance by mediating business strategy in uncertain environments. To address this question, the present study seeks to empirically explore the relationship between strategic information systems and corporate performance by mediating business strategy. Partial Least Squares-Path Modeling (PLS-PM) confirmed SIS's strong influence on strategy, and full strategy mediation on the relationship between SIS and performance. SIS showed greater performance contribution in high heterogeneity environments than in lower ones, and small and medium-sized firms have 50% more contribution of the effects of strategy on performance than large firms. The post-hoc-analysis study did not identify the presence of heterogeneity segmentation not observed by Finite Mixture (FIMIX-PLS). Through fuzzy set qualitative comparative analysis (fsQCA), non-linear causality was verified in the strategy in certain solutions by the variables of large firms, with intensive use of SIS and high environmental heterogeneity. Moreover, the study demonstrated that SIS’s strategic alignment has strong effects and explanation power on performance and may suggest that it is an indissociable resource for the strategy-as-practice effectiveness. Hence, the study contributed to understanding how SIS create value to strategy-as-practice approach under environmental turbulence to impact corporate performance.
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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 | 0.002 | 0.002 |
| 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 | 0.004 | 0.013 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".