Beyond popularity: A user perspective on observable behaviours in a digital platform
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it