
AbstractThis article explores some of the critical challenges facing self-regulation and the regulatory environment for digital platforms. We examine several historical examples of firms and industries that attempted self-regulation before the Internet. All dealt with similar challenges involving multiple market actors and potentially harmful content or bias in search results: movies and video games, radio and television advertising, and computerized airline reservation systems. We follow this historical discussion with examples of digital platforms in the Internet era that have proven problematic in similar ways, with growing calls for government intervention through sectoral regulation and content controls. We end with some general guidelines for when and how specific types of platform businesses might self-regulate more effectively. Although our sample is small and exploratory, the research suggests that a combination of self-regulation and credible threats of government regulation may yield the best results. We also note that effective self-regulation need not happen exclusively at the level of the firm. When it is in their collective self-interest, as occurred before the Internet era, coalitions of firms within the same market and with similar business models may agree to abide by a jointly accepted set of rules or codes of conduct.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 102 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 1% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 1% |
