The critical success factors (CSFs) for Enterprise Software contract negotiations
Bibliographic record
Abstract
Purpose – As the wrong Enterprise Software (ES) acquisition can lead an organization with chronically exceeded budgets and settling for minimum returns, so can an unfavorable contractual agreement. Often the acquiring organizations become vulnerable to risks and mistakes as the software contracts are habitually written using legal terminologies and mainly to the advantage of the vendor. To avoid costly ES contracting mistakes, the purpose of this paper is to empirically identify the critical success factors (CSFs) of contracting in the context of ES acquisition. Design/methodology/approach – A questionnaire survey was conducted to gather the data for this study. Statistical analysis conducted for this study include descriptive statistics, factor analysis with reliability and validity tests and nonparametric test. Findings – The five key factors are: contractual assurance, forward compatibility and licensing; right to use, own and use of own, confidentiality and payment; software acceptance; license assignment; and vendor obligation for intellectual property. The research and managerial implications of these factors are given in discussion. Research limitations/implications – As with most empirical studies, the subjectivity of the opinion of respondents from only two industries presents some limitations to generalization. Another limitation is the respondent has been asked for the degree of criticality for each of the contracting issue given in the questionnaire. There could be critical issues other than the listed ones which are more specific to the organization. Practical implications – The results can be used by managers to improve their understanding on the critical contractual issues in ES acquisition negotiations. Originality/value – The significant value of this study identifies the CSFs for ES contract negotiations while acquiring the software.
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 | 0.013 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".