Why Break the Habit of a Lifetime? Rethinking the Roles of Intention, Habit, and Emotion in Continuing Information Technology use1
Bibliographic record
Abstract
One of the most welcome recent developments in Information Systems scholarship has been the growing interest in individuals’ continuing use of information technology well after initial adoption, known in the literature as IT usage, IT continuance, and post-adoptive IT usage. In this essay, we explore the theoretical underpinnings of IS research on continuing IT use. Although the IS literature on continuing IT use emphasizes the role of habitual behavior that does not require conscious behavioral intention, it does so in a way that largely remains faithful to the theoretical tradition of planned behavior and reasoned action. However, a close reading of reference literatures on automatic behavior (behavior that is not consciously controlled) and the influences of emotion on behavior suggests that planned behavior and reasoned action may not provide the best theoretical foundation for the study of continuing IT use. As a result, we call for empirical research that directly compares and contrasts the consensus theory of continuing IT use with rival theories that place much greater emphasis on unplanned and unreasoned action.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".