Bibliographic record
Abstract
Considerable information systems (IS) research has sought to understand the adoption, implementation, and use of information systems. In contrast, the literature offers only limited insight into end-of-life issues such as those surrounding the nature of, and basis for, organizational IS discontinuance. This situation, in conjunction with the dramatic impact that discontinuance can have on organizational performance and measures of system success, suggests a strong need for further research. Since the absence of sound theoretical frameworks can impede such research, this paper offers a theoretical model of IS discontinuance that seeks to account for organizational intention to discontinue the use of an information system. The model is based on the premise that forces contributing to the formation of discontinuance intentions are opposed by inertial tendencies in favor of the status quo. Environmental change and organizational initiative are posited to be the two broad forces driving an information system toward the end of its useful life. Organizational investments in the system, system embeddedness within the organization, and mimetic isomorphism are then seen to constrain the extent to which change forces lead to the emergence of organizational discontinuance intentions. A series of propositions are offered and related guidance is provided for those interested in pursuing further research. An exploration of how the proposed model can be generalized to other discontinuance decisions such as the decision to terminate the use of an IS standard or management practice is also offered for interested readers.
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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".