The Role of Vendor Legitimacy in IT Outsourcing Performance: Theory and Evidence
Bibliographic record
Abstract
Information technology outsourcing (ITO) relationships today are facing increasingly turbulent environments. This research examines ITO performance by focusing on client firms’ perceived legitimacy of vendors, termed “vendor legitimacy,” consisting of pragmatic, cognitive, and moral dimensions. Based on our surveys with executives and managers at 200 ITO client firms, the study’s findings present the imperative to actively manage vendor legitimacy for achieving and sustaining ITO performance. Specifically, at the strategic level, clients’ perception of vendors as mutually aligned, long-term-oriented, tightly integrated partners is critical. At the operational level, clients should collaborate with vendors to design and establish interorganizational routines that undergird vendor legitimacy. At the managerial level, clients’ relational governance plays a pivotal role in attaining procedural justice, ethical standards, and fairness in the interorganizational collaboration. In sum, our study suggests that creating a dedicated corporate function or unit for continually overseeing and assessing a portfolio of vendors and swiftly identifying and responding to potential issues and crises related to vendor legitimacy would be a worthwhile investment.
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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 | 0.031 | 0.119 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".