Two faces of decomposability in organizational search: Evidence from singles versus albums in the music industry 1995–2015
Bibliographic record
Abstract
Abstract Research Summary This study proposes that decomposability may generate a trade‐off in search. This study compares a decomposed search (i.e., producing and evaluating a decomposed module) and an integrated search (i.e., producing and evaluating a full‐scale product). While the former can allow firms to experiment with more alternatives than can the latter, it may be more vulnerable to imperfect evaluation because a larger number of promising alternatives could be omitted after the initial evaluation. The reason for this is that not only do more alternatives face an unlucky draw in their initial evaluation but also a decomposed search may lead firms to set a higher performance target for giving a second‐chance opportunity. I test this theory and mechanisms by comparing singles (i.e., decomposed modules) and albums (i.e., full‐scale products) in the music industry. Managerial Summary This study highlights a hidden cost of experimentation‐oriented practices: an increased chance of terminating investment in promising business options (e.g., resources, technologies, and new business projects) after initial small‐scale experimentation. A growing number of technological innovations (e.g., software development kits, cloud computing, and e‐commerce platforms) have enabled firms to experiment with new business options by producing modules rather than full‐scale products. These innovations benefit management practices for experimentation, such as lean start‐up or design thinking, and have thus gained popularity among practitioners. This study suggests that while producing and evaluating a module enables firms to experiment with more options, it may increase the chance of terminating investment in promising business options because firms may set a higher performance target for subsequent investment after initial small‐scale experimentation.
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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 | 0.009 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".