Beyond popularity: A user perspective on observable behaviours in a digital platform
Bibliographic record
Abstract
Abstract The opinions and behaviours of others are recognised as powerful mechanisms for social influence in the digital sphere. The former, often referred to as electronic word of mouth (eWOM), is a thoroughly researched topic in the Information Systems literature. Conversely, the digital display of users' behaviours (e.g., number of past purchases) is less well understood despite the widespread adoption of this practice on digital platforms. Quantitative research has explored this interesting domain and found that observing others' behaviours entice observers to follow suit, but has left unaddressed the question of what sensemaking users derive from behavioural information. This is problematic as behavioural information is more open to interpretation compared to eWOM. In this article, we adopt the concept of electronic word of behaviour (eWOB) to denote such behavioural information. Through the lens of basic psychological needs theory and the qualitative means‐end chain approach, we expose how eWOB is interpreted and used by users of a digital platform, the music service Spotify. We find that eWOB leads to satisfaction of the basic psychological needs for relatedness and competence when observing others' behaviours. We also show how exposure to one's own past behaviours can yield a positive sense of self when presented in meaningful and private manners, but that it can also negatively impact users when their needs for autonomy and competence are not heeded by the digital platform. Finally, based on our empirical findings we offer a set of design implications for how digital platforms can optimise the use of eWOB.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".