ERP systems and management accounting
Bibliographic record
Abstract
Purpose This paper aims to show how our understanding of the effects of enterprise resource planning (ERP) systems on management accounting are influenced through “nudging” by researchers in their preamble before interviews begin. Design/methodology/approach There were two groups of comparable respondents. Each group received a different preamble to the same questions. The differences in group responses were analyzed. Findings When the impact of ERP implementation on the physical, transactional and information flows within the firm were nudged, the responses focused on how the chart of accounts had to be expanded to account for the additional data introduced by transaction processing. When the IT and ERP system knowledge and skills were nudged, the responses tended to emphasize analyses or the use of new information through the use of drill down functionality. This research provides new insights and contributions to understanding how nudging affects or directs respondent assessments of the impact of ERP systems on management accounting. Research limitations/implications The research is limited by the relatively small samples and by the fact that these were different research projects. Practical implications Nudging has an obvious impact on research that should not be ignored. Social implications Unintentional nudging should be considered with all research projects. Originality/value This paper makes explicit that nudging occurs in research whether intentional or unintentional.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".