The Role of Information Seeking and Use in Management Accounting: Exploring the Process of Creating a Budget for Complex Projects.
Bibliographic record
Abstract
This research study investigates the relatively unexplored topic of the ‘process’ of creating a budget. In this study, the focus is on preparing the budget for use, as opposed to using the budget as a tool once it has been prepared. This study on the budget creation process, a topic in management accounting studies, draws upon research from the information science domain. Understanding the process of creating a budget, i.e., how information is located, evaluated, and used, can be significant as the quality of the budget as a product (the output from the process) can have a downstream effect on the decisions for which the budget may be an input. This study applies both information science and management accounting literature to help improve our understanding of the budgeting process. A preliminary conceptual framework, informed by the review of literature in both management accounting (budgeting) and information science (information needs, seeking, and use), was designed to be used as a guiding star/map. The preliminary conceptual framework evolved through interviews with 22 industry professionals. A semi-structured interview approach was utilized in an individual 1:1 interview setting. This empirical method allowed the participant to tell their story, while ensuring that prompts and a few prepared structured questions were provided to help guide the conversation. Six main themes (task readiness and organization, information needs, sources of information gathered, information seeking, information use, influence of experience) surfaced from the analysis of the participant interview data. The research contribution from this study is a Conceptual Framework for Budgeting Process that can be used while working through the process of creating a budget. The conceptual framework produced from this study is a unique offering of a roadmap to understand the “role of information seeking and use in management accounting” during the process of creating a budget. The outcomes of this research study also contribute by adding an important critical thinking element to the management accounting literature. This research study also suggests further possibilities for research linking information behavior to other management accounting topics or other accounting disciplines, e.g., auditing and assurance, financial accounting, that can be explored.
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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 | 0.032 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".