Different departments, different drivers
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the antecedents and performance consequences of voluntary information exchange between the production and sales functions. Design/methodology/approach Building on the motivation-opportunity-ability framework, the authors first posit a general model for bilateral information exchange across functional levels. The innovation presented in this model consists in allowing both sides of such an exchange (e.g. production-to-sales and sales-to-production) to differ in the perceived adequacy of information they receive. The two sides can also differ in terms of how their motivation and ability impact that adequacy. To test the model, the authors make use of survey responses and objective data from sales, production and executive managers of 182 Chinese manufacturers. Findings Analysis of the sample shows that the sales-to-production exchange has a smaller estimated performance effect than the production-to-sales exchange. Although shared opportunity is important in predicting both sides of the exchange, the measure of motivation appears to only significantly impact the sales-to-production exchange. In contrast, the measure of ability only appears to significantly affect the production-to-sales exchange. Research limitations/implications Although limited to a regional context, differences in information-sharing drivers on the two sides of production-sales dyads pose strong implications that may be generalizable. Practical implications Specifically, these findings suggest alternative approaches and foci for resource investment that higher level managers can leverage in developing more effective cross-functional work settings. Originality/value This study differentiates itself from extant literature on information sharing by focusing on cross-functional (vs intra-functional) and voluntary (vs routine) information exchange.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.008 |
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".