The Effect of Complementarities Between IT and Organizational Resources on Firm Performance
Bibliographic record
Abstract
The business value of IT, as measured through its impact on organizational performance, has been hard to pin down. Previous studies, employing a variety of methods, have produced mixed results. This thesis proposes a modified approach for ascertaining IT-related benefits. Based on the resource-based view (RBV) of the firm and the general systems theory (GST), this thesis develops and tests a conceptual model exploring the effect of complementarities between IT and organizational resources on firm performance. The model is being tested using data from a large scale cross-sectional survey of firms from multiple industries. The thesis also develops a framework that identifies the properties of IT and organizational resources that enhance the propensity for mutual compatibility and complementarity. This framework enables the determination of potential IT business value that, in turn, leads to better informed decisions regarding IT investments. Overall, the thesis extends the RBV to include the evolution of strategic resources, provides a tool for practitioners with which to evaluate IT benefits, and adds to the existing knowledge on the impact of IT on organizational performance.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".