Risk Implications for the Role of Budgets in Implementing Post-Acquisition Systems Integration Strategies
Bibliographic record
Abstract
This paper studies the role of budgets in implementing the systems integration strategies in an Australian post-acquisition case of two organisations and reducing its associated often-regarded high risks. It attempts a fresh narrative approach to examine the evolution of accounting and its effects on the challenges of post-acquisition integration processes by using the performative approach such as the sociotechnical networks of Actor Network Theory in a broader analytical framework as a possible solution to reducing the risks inherent in systems integration. The methodology of the case study is based on Callon’s model of Four-Moment translation where integration strategy and budgets are regarded as social practice and defined relationally as bundles of activities and take form in and through practice and interaction between diverse actors and actants. A qualitative approach is adopted in the examination of the systems integration networks in an Australian post-acquisition case. Data was collected and analysed using semi-structured interviews. It was found, through the examination of the routine practices of systems integration strategy making and how people enact and draw on a certain financial report on a daily basis to perform systems integration network strategies, that material forms of accounting act as a powerful structuring and inscription tool in integration activities, thus shaping integration strategic options and post-acquisition economic conditions of the organisation. The result shows how the risk could be reduced in the post-acquisition system integration. The research contributes to the risk, change, and accounting literatures by providing insights into the mundane and ordinary practices of different aspects of integration strategy making, and the way employees enact and draw on accounting numbers on a day-to-day basis to perform systems integration network strategies. This case study facilities this research to be further developed and broadened in terms of other cases, industries, and countries.
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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 | 0.022 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".