Managerial Influence in the Implementation of a New Technology
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
AbstractIn the implementation of an organizational innovation, managers are usually presumed to influence the extent to which the innovation is adopted and used by their subordinates. However, the findings presented in this paper suggest that the managerial influence is not equally perceived by all subordinates. Rather, certain context-specific characteristics of individual employees mediate the managerial influence. Users of the expert system studied herein who were low in personal innovativeness toward this class of innovations, for whom the subjective importance of the task being computerized was low, whose task-related skills were low or who were low performers in their sales job—all these user groups perceived their management had encouraged them to adopt. In contrast, users who rated high on any of these measures did not perceive any management influence in their adoption decision. Moreover, although access to the innovation was in fact highly similar for all users, high performers also were inclined to perceive the system as more accessible than were low performers. These findings suggest that the diffusion of an innovation within an organization perhaps could be viewed as a two-step managerial process. Employees whose characteristics incline them to adopt an innovation will do so without management support or urging if it is simply made available. Employees low on these characteristics will await a managerial directive before adopting. Implications for future research are discussed.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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