Product configuration, ambidexterity and firm performance in the context of industrial equipment manufacturing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it