Creating and maintaining digital third places: Orchestrating interaction ritual chains at a distance
Bibliographic record
Abstract
With online and offline lives increasingly intertwined, hybrid retail spaces are emerging as new social hubs akin to classical third places. Third places refer to spaces apart from work and home, such as cafes and bars, that provide opportunities for social interaction. While prior research has primarily conceptualized third places as physical establishments, it also acknowledges that online environments, such as multiplayer gaming platforms or chatrooms, can fulfil similar functions. Yet, despite the recognized social-supportive role of retail venues, relatively little is known about how third place atmospheres can effectively be orchestrated in online retail settings. This study addresses this gap through an ethnographic investigation of a digital platform that recreates a physical third place online, enabling consumers to gather for long hours, consuming, conversing, and socializing. We find that, in online retail settings, third place atmospheres can be cultivated through the deliberate orchestration of technology-mediated interaction ritual chains. Through three interconnected processes – ritual framing, boundary regulation, and affective synchronization – that unfold before, during, and after the gatherings, the online setting transforms into a digital third place. Both retailers and consumers play pivotal roles in this transformation. Drawing on these findings, we offer several theoretical contributions and managerial recommendations.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".