How accessories add value to a platform: the role of innovativeness and nonalignability
Bibliographic record
Abstract
Purpose This study aims to distinguish between the indispensable (software) versus discretionary (accessories) complementary products to a platform. It investigates the impact of accessories on increasing the perceived value and sales of a base platform. In particular, the role of two distinct characteristics of accessories – innovativeness and structural nonalignability – in driving the sales of the base platform. Design/methodology/approach Combining sales data from the US video gaming industry with primary data on the above two aspects of accessories, this study quantifies the effect of accessories portfolio on the sales of three brands of video gaming platforms. Findings A distinct network externality arises from accessories for video gaming platforms, above and beyond the effects of game titles. Importantly, the average level of innovativeness and nonalignability of the accessories portfolio, as well as the frequency of introduction of highly innovative and/or nonalignable accessories positively impact the sales of the platform. Research limitations/implications This research seeks to address the gap in the innovation literature on the role of discretionary complementary products (i.e. accessories) on platform sales. Future research should examine this in other platform contexts as well. Practical implications Managers of platform-mediated products should give due consideration to accessories, as an important driver of the sales of the platforms. Product managers can leverage the advantage of innovative and nonalignable accessories to enhance consumer demand for the platform. Originality/value This study is the first to conceptualize and empirically verify the network externality arising from accessories, a heretofore much neglected component of platform-based markets.
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 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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".