Exploring the differences between accountants and marketers in terms of information sharing
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the nature of information sharing, rewards and selected performance measures based on the dyadic relationship between accountants and marketers. Design/methodology/approach Mail survey administered to senior executives of larger Canadian firms. MANOVA followed by univariate analyses are used to identify significant dimensions. Variables that are important to distinguish between the two groups are identified using logistic regression. Findings Accountants are more satisfied with the quality of shared information and rate its impact on performance higher than marketers. Marketers view accountants as a more important source of information. Research limitations/implications Longitudinal studies, in‐depth surveys within a single firm and employing respondents at different hierarchical levels would provide important insights and reduce common‐method bias. Practical implications Accountants should recognize marketing as an important source of information since resource complementarity is crucial to collaborative success. Using market‐based reward systems and establishing quality as an important goal would have a bigger impact on marketers in enhancing information sharing between the two functions. Originality/value Contributes to filling the gap regarding the nature of information sharing between marketing and accounting as well as its relationship to market‐based rewards and selected performance measures.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".