THE CONVENIENCE STORE REVOLUTION: COMPUTER NETWORKS, LOGISTICS, AND THE REINVENTION OF RETAIL IN JAPAN
Bibliographic record
Abstract
Convenience stores in their current, most globally popular form were born in the US, reinvented in Japan, and re-exported to Asia and the world. No company better illustrates this transnational trajectory than 7-Eleven. This paper turns to the humble, often-overlooked convenience store as a crucial site for thinking critically, historically, and globally about the discourse of Internet revolutions. In Japanese language business literature and popular descriptions, 7-Eleven Japan’s innovative use of networked computing and logistics from the 1980s onwards led (among other factors) to its immense national and then international success. In this paper I will draw on my archival research into the convenience store in Japan to argue for that this is a key site from which to rethink histories of networked computing and the Internet “revolution” in a non-Western context – furthering the project of “de-Westernizing” or de-colonizing Internet studies. Building on existing research on Internet histories in East Asia, this paper turns to the convenience store industry and 7-Eleven Japan in particular to tell a different story of the Internet itself. Many contemporaneous accounts of 7-Eleven’s practices in the 1980s and 1990s treat its turn to information-gathering, networked computing, logistics, and point of service ordering systems as revolutionary developments. As such the convenience store offers an alternative account of commercial revolutions and networked computing. It also offers a different view of contemporary discourses of “convenience” by retailers such as Amazon, an infrastructural or logistical view of convenience provision, and a new way of narrating Internet history.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".