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Changing Their Tune: How Consumers’ Adoption of Online Streaming Affects Music Consumption and Discovery

2017· article· en· 273 citations· W3123243604 on OpenAlex· 10.1287/mksc.2017.1051

Why is this work in the frame?

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

Canadian funderA Canadian agency funded it. The work may carry no Canadian affiliation at all.

No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Full frame distilled prediction

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.

Candidate categories
Scholarly communication
Consensus categories
none
Domain
Candidate signal: noneConsensus signal: none
Study design
Candidate signal: ObservationalConsensus signal: none
Genre
Candidate signal: EmpiricalConsensus signal: Empirical
Teacher disagreement score
0.617
Threshold uncertainty score
1.000
Validation status
machine_predicted_unvalidated · codex-gemma-dda1882f352a

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

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.

Opus teacher head0.036
GPT teacher head0.239
Teacher spread
0.203 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Abstract

Instead of purchasing individual content, streaming adopters rent access to libraries from which they can consume content at no additional cost. In this paper, we study how the adoption of music streaming affects listening behavior. Using a unique panel data set of individual consumers’ listening histories across many digital music platforms, adoption of streaming leads to very large increases in the quantity and diversity of consumption in the first months after adoption. Although the effects attenuate over time, even after half a year, adopters play substantially more, and more diverse, music. Relative to music ownership, where experimentation is expensive, adoption of streaming increases new music discovery. While repeat listening to new music decreases, users’ best discoveries have higher play rates. We discuss the implications for consumers and producers of music. Data and the online appendix are available at https://doi.org/10.1287/mksc.2017.1051 .

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.

The record

Venue
Marketing Science
Topic
Copyright and Intellectual Property
Field
Business, Management and Accounting
Canadian institutions
not available
Funders
Hyderabad Eye Research FoundationUniversity of British Columbia
Keywords
Active listeningConsumption (sociology)Music industryEarly adopterDigital audioPurchasingDiversity (politics)Set (abstract data type)AdvertisingBusinessComputer scienceMarketingTelecommunicationsPsychologySociology
Has abstract in OpenAlex
yes