Using organizational influence processes to overcome IS implementation barriers: lessons from a longitudinal case study of SPI implementation
Bibliographic record
Abstract
A 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".