Globally distributed R&D work in a marketing management support systems (MMSS) environment: a knowledge management perspective
Bibliographic record
Abstract
Globalisation, liberalization and rapid technological developments have been changing business environments drastically in the recent decades. These trends are increasingly exposing businesses to market competition and thus intensifying competition. In such an environment, the role of marketing management support systems (MMSS) becomes exceedingly important for the long‐term growth of an organisation’s marketing expertise and success. In this paper, we discuss the evolution of a globally distributed R&D project spanning three continents in developing an MMSS for the motion picture industry. We first provide the conceptual background of the MMSS and knowledge management systems relevant for our work. We then provide a detailed case study of our MMS implementation. We specifically focus on the following elements of our work: globally distributed R&D efforts, knowledge elements, and fit between demand and supply sides of MMSS. We conclude with a discussion of implications for future research in this area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".