Exploring the impact of top management support of enterprise systems implementations outcomes
Bibliographic record
Abstract
Purpose Despite the general consensus regarding the critical role of top management in the information systems (IS) implementation process, the literature reveals a lack of understanding of top managers' supportive actions. There are also conflicting findings, which, based on the review, are the result of unexamined perspectives (deterministic, contingent, and dynamic perspective) of the impact of top management support (TMS). The purpose of the study is to compare the applicability of the three perspectives to enrich the understanding of TMS under the context of enterprise systems (ES) implementation. Design/methodology/approach Case studies were conducted in two Canadian universities which have implemented a large‐scale ES to examine the applicability of the three perspectives. About 19 interviews were conducted with top managers, department managers, project managers, users and trainer. Findings Results reveal that the deterministic and contingent perspective may be a simplified version of a complex picture and may not reflect how top management actions affect implementation outcomes. The case study indicates that top managers followed the dynamics of the IS implementation process. Practical implications The case studies offer several important findings to practitioners. For example, top managers need to constantly obtain feedback from users and adjust their supportive actions and the level of these supportive actions accordingly. Originality/value Despite the consensus on the importance of TMS, TMS studies hold different perspectives of the impact of top managers' supportive actions on IS implementation outcomes. By comparing the three perspectives, the study makes important contributions to both academic researchers and practitioners.
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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 | 0.025 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".