Continuance Intention to use High Maintenance Information Systems: The Role of Perceived Maintenance Effort
Bibliographic record
Abstract
Information Systems (IS) continued use theories have typically excluded a role for effort expectancy and similar constructs arguing that they do not impact the intentions of experienced users. This may not hold true for an emerging class of information systems that we refer to as High Maintenance Information Systems (HMIS). HMIS are a class of information systems that require users to expend an ongoing maintenance effort in order to keep the system up-to-date so they can continue to reap future benefits out of using the system. This ongoing maintenance effort is unlikely to significantly diminish as users gain further experience. The proposed study seeks to develop a theoretical model that explains the factors influencing individualsâ continued use of such systems taking into account the potential role of perceived maintenance effort. The proposed model will be validated using a survey design involving experienced Facebook users. Collected data will be analyzed using structural equation modeling and qualitative data analysis techniques.
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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 | 0.008 | 0.011 |
| 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 | 0.001 | 0.005 |
| Open science | 0.001 | 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".