Enhanced Use of IT: A New Perspective on Post-Adoption
Bibliographic record
Abstract
A major problem confronting organizations is that they make large investments in information technologies (IT) that, in many cases, underperform following adoption because their features are underutilized. In information systems (IS) research, there is a need to develop a better understanding of the process by which individuals make new use of IT features. Using a grounded theory approach, we develop such an understanding by closely examining how individuals change their IT use following initial adoption. Based on analyzing interview data and expanding on extant literature to refine our results, we propose a construct called “enhanced use”, which refers to novel ways of employing IT features. We conceptualize enhanced use as having distinct forms (using a formerly unused set of available features, using an IT for additional tasks, and/or using extensions of IT features and attributes). Our analysis reveals that these forms may differ in terms of their attributes (locus of innovation, extent of extensive use, and adaptation). Our study uncovers patterns of use that reveal the roles played by task characteristics, knowledge, and the IT type in shaping enhanced use. Thus, this study heeds repeated calls to theorize about use by proposing a novel and rich conceptualization of post-adoption use.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".