A Multilevel Model of Resistance to Information Technology Implementation1
Bibliographic record
Abstract
To better explain resistance to information technology implementation, we used a multilevel, longitudinal approach. We first assessed extant models of resistance to IT. Using semantic analysis, we identified five basic components of resistance: behaviors, object, subject, threats, and initial conditions. We further examined extant models to (1) carry out a preliminary specification of the nature of the relationships between these components and (2) refine our understanding of the multilevel nature of the phenomenon. Using analytic induction, we examined data from three case studies of clinical information systems implementations in hospital settings, focusing on physicians’ resistance behaviors. The resulting mixed-determinants model suggests that group resistance behaviors vary during implementation. When a system is introduced, users in a group will first assess it in terms of the interplay between its features and individual and/or organizational-level initial conditions. They then make projections about the consequences of its use. If expected consequences are threatening, resistance behaviors will result. During implementation, should some trigger occur to either modify or activate an initial condition involving the balance of power between the group and other user groups, it will also modify the object of resistance, from system to system significance. If the relevant initial conditions pertain to the power of the resisting group vis-à-vis the system advocates, the object of resistance will also be modified, from system significance to system advocates. Resistance behaviors will follow if threats are perceived from the interaction between the object of resistance and initial conditions. We also found that the bottom-up process by which group resistance behaviors emerge from individual behaviors is not the same in early versus late implementation. In early implementation, the emergence process is one of compilation, described as a combination of independent, individual behaviors. In later stages of implementation, if group level initial conditions have become active, the emergence process is one of composition, described as the convergence of individual behaviors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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 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".