The Effect of Online Engagement on New Product Performance: Why Fit and Brand Longevity Matter
Bibliographic record
Abstract
Recently, customer engagement in social media has received great attention in the literature, with the aim of understanding its impact on brands and product performance. However, little attention has been given to the potential dark side of engagement, especially in the context of new product launches. This article thus examines the relationships among customer engagement in social media, new product fit, brand longevity, and new product performance. Using the music industry as a context, this research shows that the small but positive effect of prerelease social media engagement on record sales becomes strong when the level of fit is high; for newer artists, engagement can even have a negative effect when fit is very low. This study uses a sample of 181 albums launched by 158 artists in the Canadian market between 2016 and 2017 and a data set that combines weekly record sales, social media activity, and Spotify's audio features analysis. A regression discontinuity–inspired model that accounts for endogeneity concerns is applied to test the hypotheses. This study contributes to the literature by providing robust empirical evidence of a possible negative side of engagement. Although engagement can help artists succeed, it might interfere with their artistic freedom.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".