An Exploration of the Drivers of Non-Adoption Behavior
Bibliographic record
Abstract
While there has been a substantial amount of attention within the information systems research community towards understanding the phenomenon of adoption, much less is known about non-adoption. This study examines the factors surrounding the decision to not adopt a technology and whether certain factors exert differing effects on individuals in particular ways such that concurrent factors could be identified to develop a classification of the specific types of non-adoption behavior. Utilizing inhibitor theory and the symbolic adoption model as a foundational framework for the different types of non-adoption, we posit that different types of non-adoption exist which is demonstrated by determining the perceptions towards technology that coalesce around different types of non-adoption. We conducted a two-phase investigation into non-adoption with two goals in mind: 1) identify and explore specific factors of the IT that are associated with the rejection decision and are distinct from the adoption decision, and 2) determine the extent to which these factors (along with traditional enablers) differentiate between different types of non-adoption. The results from a discriminant function analysis (DFA) indicate the coalescence of specific perceptual variables according to the types of non-adoption behavior, specifically, the discriminatory power of differing perceptions of IT between trial rejecters, symbolic rejecters, trial accepters, symbolic adopters, and adopters. The implications for research and implications for practice are discussed.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.016 |
| Open science | 0.002 | 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".