The effects of person–innovation fit on individual responses to innovation
Bibliographic record
Abstract
Drawing on the person–environment fit literature, we propose that cognitive comparisons between person and innovation on meaningful dimensions determine organizational members' affective and behavioural responses to innovations. Specifically, we hypothesize that two different types of person‐innovation fit constructs (value fit and ability fit) differentially predict employees' commitment to implementation and implementation behaviour. The results of this study indicate that congruence between innovation values and personal values is more strongly related to employees' commitment to implementation than to implementation behaviour, whereas the congruence between required abilities and current abilities is more strongly associated with implementation behaviour than with commitment to implementation. In addition, commitment to implementation was more strongly associated with environmental characteristics (innovation values), whereas implementation behaviour was associated more strongly with personal characteristics (personal values, current abilities). This study expands the person–environment fit and innovation implementation literature by applying the fit concept to a new domain and by identifying and testing cognitive processes that determine employees' affective and behavioural responses to innovations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".