An Exploration of Organizational Level Information Systems Discontinuance Intentions
Bibliographic record
Abstract
Limited attention has been directed toward examining post-adoption stages of the information system life cycle. In particular, the final stages of this life cycle have been largely ignored despite the fact that most systems eventually reach the end of their useful life. This oversight is somewhat surprising given that end-of-life decisions can have significant implications for user effectiveness, the value extracted from IS investments, and organizational performance. Given this apparent gap, a multi-method empirical study was undertaken to improve our understanding of organizational level information system discontinuance. Research commenced with the development of a broad theoretical framework consistent with the technology–organization– environment (TOE) paradigm. The resulting framework was then used to guide a series of semi-structured interviews with organizational decision makers in an effort to inductively identify salient influences on the formation of IS discontinuance intentions. A set of research hypotheses were formulated based on the understanding obtained during these interviews and subsequently tested via a random survey of senior IS decision makers at U.S. and Canadian organizations. Data obtained from the survey responses was analyzed using partial least squares (PLS). Results of this analysis suggest that system capability shortcomings, limited availability of system support, and low levels of technical integration were key determinants of increased intentions to replace an existing system. Notably, investments in existing systems did not appear to significantly undermine organizational replacement intentions despite support for this possibility from both theory and our semi-structured interviews.
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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 | 0.010 | 0.032 |
| 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 | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".