Product configuration, ambidexterity and firm performance in the context of industrial equipment manufacturing
Bibliographic record
Abstract
Abstract The practice of configuring products to individual customer orders has found application in a variety of industry contexts, but little is known about the specific capabilities that firms develop to successfully compete when offering configurable products. Our research begins to fill this gap in the context of industrial equipment manufacturing. Drawing from the ambidexterity literature, we argue that firms have to balance dual goals of reducing variation and promoting variation in their product configuration activities by fostering two distinct firm‐level capabilities: product configuration effectiveness (PCE) and product configuration intelligence (PCI). Specifically, we hypothesize that the simultaneous presence of PCE and PCI—that is, product configuration ambidexterity (PCA)—drives superior firm responsiveness and, indirectly firm sales and operating margin. However, we also contend that responsiveness gains through PCA can diminish with product complexity and can increase operating cost. We test these hypotheses by collecting both primary and secondary data from a sample of 108 European industrial equipment manufacturing firms. Results from our analyses indicate that PCA has an indirect effect through responsiveness on sales and operating cost but not on operating margin, with this effect diminishing with product complexity. Taken together, our results suggest that investment in developing PCA may represent a conundrum for industrial equipment manufacturing firms, because it translates into market but not financial advantages, and it is intertwined with product design decisions. We conclude this study with a discussion of the findings for theory and practice.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".