Organizing Marketing in Matrix Structured Companies, a Case Study of Alfa Laval
Bibliographic record
Abstract
Purpose: To find out how marketing should be organized in a matrix structured organization in order to increase the company's overall efficiency Methodology: A qualitative single case study with in-depth interviews as the main source for empirical data.The use of a theoretical framework guided the research Empirical Foundation: 25 interviews were conducted with employees at diverse positions at Alfa Laval.A conducted survey also collected complementary answers by 263 respondents. Conclusions:The complexity of a large global matrix structured company will be apparent also in the marketing work.For this complexity to be manageable it is important to have a clear division of responsibilities, and managers whose role is to take responsibility for the marketing.The matrix structure needs to be continuously updated along with external and internal changes in order to stay efficient and agile.A company with a heritage of being innovation driven and product focused might increase its overall performance by working with a customer focus.We would like to take the opportunity to thank Alfa Laval and all of the respondents participating in our research, both in the interviews and in the survey.The knowledge they have shared with us has been necessary for conducting this study.But most of all we would like to thank Robert Barnes (Marketing Planning Manager, Marketing Processes at Alfa Laval) for believing in us and giving us the opportunity to do our master thesis at Alfa Laval.We are grateful that you gave us access to the empirical material and most of all for giving us, and our research study, so much of your time.Furthermore we want to thank our supervisor Christian Koch for constructive and helpful feedback throughout this semester, for time and patience, and Lund University and course coordinator Jens Hultman for helpful guidance and encouragement.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".