Bibliographic record
Abstract
Purpose ERP systems continue to fail. One success factor that has received little attention in the literature is cultural fit – which emphasizes the need for ERP systems to be chosen and adapted to current organizational practices. However, the dynamics behind culture and its fit with ERP require investigation. This paper aims to fills this gap. Design/methodology/approach The paper draws upon cultural and linguistic concepts from Antonio Gramsci to consider how consent is achieved in ERP implementation projects. These concepts include positive (integral) and negative (decadent and minimal) hegemony, as well as the production and effects of normative and spontaneous grammars. The paper examined the implementation of an ERP in a logistics company, using interview and documentary evidence. Findings The findings reveal that, while consensus is apparently achieved across disparate groups and interests, it is achieved through the use of phrases which marginalized groups by their abstract and rhetorical nature. This implementation process allowed for the subordination of local interests, making it difficult to form alternative responses. It is concluded that decadent and minimal hegemonies prevailed, instead of an integral hegemony formed through continuous negotiation and debate across sub‐groups. Research limitations/implications The paper suggests that studies of ERP implementation using Gramsci's concepts of negative (minimal and decadent) and positive (integral) hegemonies, that influence cultural fit, can aid the study of positive and negative forms of consent. Practical implications The paper illustrates how cultural fit during ERP implementation could be achieved through technical and cultural change‐based grammars and languages which allow broad democratic participation. Originality/value This paper illustrates the value of Gramsci's concepts in IS research, and provides valuable insights into the dynamics of “cultural fit”.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.055 |
| Scholarly communication | 0.013 | 0.026 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".