Enhanced Use of IT: A New Perspective on Post-Adoption
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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 it