A CAPABILITY MATURITY MODEL OF INFORMATION TECHNOLOGY OUTSOURCING RELATIONSHIPS: A VENDOR PERSPECTIVE
Bibliographic record
Abstract
Information technology outsourcing relationships between clients and suppliers are generally embedded into formal contracts. The empirical literature on contracting usually assumes that contractual completeness is difficult to achieve due to the transaction costs of describingâor of even foreseeingâthe possible states of nature in advance. Little research has been done on the on-going set of processes during the life of the client/supplier relationship in the post phase of the contract where unforeseen contingencies and events emerge. Hence, a process improvement framework for studying IT outsourcing relationships is needed to provide more detailed metrics for assessing and managing the maturity level of IT outsourcing relationships. The purpose of this paper is to develop and validate an evolutionary process improvement model to manage relationships between clients and suppliers in the context of information technology outsourcing. We call this model the Capability Maturity Model of Information Technology Outsourcing Relationships (CMMITOR). The model presents the key elements of maturity in IT outsourcing relationships from an ad hoc stage, immature relationship management processes to highly mature and disciplined ones. The preliminary results show that the process-view of IT relationships is suited for this purpose. Key process areas were identified and refined through a card sorting procedure. The validation of the resultant model is underway through a field study of ten IT outsourcing relationships. Implications for research and practice are discussed.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| 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 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".