Information Technology Implementers’ Responses to User Resistance: Nature And Effects1
Bibliographic record
Abstract
User resistance has long been acknowledged as a critical issue during information technology implementation. Resistance can be functional when it signals the existence of problems with the IT or with its effects; it will be dysfunctional when it leads to organizational disruption. Notwithstanding the nature of resistance, the implementers—business managers, functional managers, or IT professionals—have to address it. Although the literature recognizes the importance of user resistance, it has paid little attention to implementers’ responses—and their effect—when resistance occurs. Our study focuses on this phenomenon, and addresses two questions: What are implementers’ responses to user resistance? What are the effects of these responses on user resistance? To answer these questions, we conducted a case survey, which combines the richness of case studies with the benefits of analyzing large quantities of data. Our case database includes 89 cases with a total of 137 episodes of resistance. In response to our first research question, we propose a taxonomy that includes four categories of implementers’ responses to user resistance: inaction, acknowledgment, rectification, and dissuasion. To answer our second question, we adopted a set-theoretic analysis approach, which we enriched with content analysis of the cases. Based on these analyses, we offer a theoretical explanation of how implementers’ responses may affect the antecedents that earlier research found to be associated with user resistance behaviors.
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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 | 0.023 | 0.135 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".