Reciprocity between Senior IT Executives and IT-Capable Firms: A Source of Competitive Advantage
Bibliographic record
Abstract
This study introduces a causality-based framework of antecedents and consequences in order to examine the positive reciprocity between senior IT executives (sITes) and IT capable firms. More specifically we propose that: 1. There is a positive association between accrued sources of managerial power of sITes, such as structural and expert power, and a firm's ability to develop superior IT capability. 2. Firms that achieve such superiority are more likely to signal their appreciation and reward (promote) their sITes. 3. If sITes value this reward, they are more likely to stay longer with their firm, thus ensuring the continuity of an already successful IT leadership and a firm’s ability to sustain its IT superiority. Results based on panel data of 1326 large US firms from a wide spectrum of industries over a 13-year period (1997-2009) support our propositions. Empirical evidence validates our position that firms that want to achieve and sustain IT superiority need to create an organizational climate of positive reciprocity. Such an organization climate can only be developed over time and there is no short cut that competitors can take in order to replicate it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".