Top Management Support of Enterprise Systems Implementations
Bibliographic record
Abstract
Despite the general consensus regarding the critical role of top management in the information systems (ISs) implementation process, the literature has not yet provided a clear and compelling understanding of the top management support (TMS) concept. Applying metastructuring (Orlikowski et al., 1995) as a guiding framework for understanding TMS behaviors, this paper attempts to address the gap by focusing on two key questions: (1) What supportive actions do top managers engage in during IS implementations? (2) How do these actions affect IS implementation outcomes? Analyses of in-depth case studies at two Canadian universities that had implemented a large-scale enterprise system revealed three distinct types of TMS actions: TMS - resource provision (TMSR - actions related to supplying key resources such as funds, technologies, staff, and user training programs); TMS - change management (TMSC - actions related to fostering organizational receptivity of a new IS); and TMS - vision sharing (TMSV - actions related to ensuring that lower-level managers develop a common understanding of the core objectives and ideals for the new system). Results suggest that different support behaviors exercise different influences on implementation outcomes, and that top managers need to adjust their support actions to achieve the desired outcomes. In particular, TMSR affected project completion, TMSC impacted formation of user skills and attitudes, and TMSV influenced middle manager buy-in. Theoretical and practical implications of these findings are discussed.
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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 | 0.013 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".