Ain't it “suite”? Bundling in the PC office software market
Bibliographic record
Abstract
Research Summary: We examine the importance of office suites for the evolution of the personal computer (PC) office software market in the 1990s. An estimated discrete‐choice model reveals a positive correlation of consumer values for spreadsheets and wordprocessors, a bonus value for suites, and advantages for Microsoft products. We employ the estimates to simulate various hypothetical market structures to evaluate the profitability, welfare, and competitive effects of suites under alternative correlation assumptions. We find that firms benefit greatly from bundling components (i.e., a spreadsheet and a word processor) when the correlation of consumer preferences over the components in the bundle is positive. Our work adds another aspect to the recent work in the strategy literature that examines benefits from bundling when there are complementary relationships across the products in the bundle. Managerial Summary: Our research helps managers understand the conditions under which product bundling is likely to be most profitable. We show that one key to enhanced profitability is the correlation in consumer preferences over the individual products. We consider the performance implications of bundling under a variety of alternative market structures and competitive environments. Our analysis reveals that firms benefit greatly from bundling when the correlation of consumer valuations over the products is positive. Consumers benefit as well. Hence, bundling is a win‐win for firms and their customers. Since profits increase by more than consumer surplus, bundling leads to increased value capture by the firms. Consequently, it may be profitable for firms to invest in actively increasing the correlation in consumer preferences over products in the bundle.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".