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.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".