Using organizational influence processes to overcome IS implementation barriers: lessons from a longitudinal case study of SPI implementation
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
\n\t\t\t\t\tA fundamental tenet of the information systems (IS) discipline holds that: (a) a lack of formal power and influence over the organization targeted for change, (b) weak support from top management, and (c) organizational memories of prior failures are barriers to implementation success. Our research, informed by organization influence, compellingly illustrates that such conditions do not necessarily doom a project to failure. In this paper, we present an analysis of how an IS implementation team designed and enacted a coordinated strategy of organizational influence to achieve implementation success despite these barriers. Our empirical analysis also found that technology implementation and change is largely an organizational influence process (OIP), and thus technical-rational approaches alone are inadequate for achieving success. Our findings offer managers important insights into how they can design and enact OIPs to effectively manage IS implementation. Further, we show how the theory of organizational influence can enhance understanding of IS implementation dynamics and advance the development of a theory of effective IS change agentry.\n\t\t\t\t
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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