Social media are fundamentally changing the way we communicate, collaborate, consume, and create. They represent one of the most transformative impacts of information technology on business, both within and outside firm boundaries. This special issue was designed to stimulate innovative investigations of the relationship between social media and business transformation. In this paper we outline a broad research agenda for understanding the relationships among social media, business, and society. We place the papers comprising the special issue within this research framework and identify areas where further research is needed. We hope that the flexible framework we outline will help guide future research and develop a cumulative research tradition in this area.
User-generated content has been hailed by some as a democratizing force that enables consumers to discuss niche products that were previously ignored by mainstream media. Nevertheless, the extent to which consumers truly prefer to use these new outlets to discuss lesser-known products as opposed to spending most of their energies on discussing widely marketed or already successful products has so far remained an open question. We explore this question by investigating how a population's propensity to contribute postconsumption online reviews for different products of the same category (motion pictures) relates to various indicators of those products' popularity. We discover that, ceteris paribus, consumers prefer to post reviews for products that are less available and less successful in the market. At the same time, however, they are also more likely to contribute reviews for products that many other people have already commented on online. The presence of these two opposite forces leads to a U-shaped relationship between a population's average propensity to review a movie postconsumption and that movie's box office revenues: moviegoers appear to be more likely to contribute reviews for very obscure movies but also for very high-grossing movies. Our findings suggest that online forum designers who wish to increase the contribution of user reviews for lesser-known products should make information about the volume of previously posted reviews a less-prominent feature of their sites.
Reputation mechanisms have become an important component of electronic markets, helping to build trust and elicit cooperation among loosely connected and geographically dispersed economic agents. Understanding the impact of different reputation mechanism design parameters on the resulting market efficiency has thus emerged as a question of theoretical and practical interest. Along these lines, this note studies the impact of the frequency of reputation profile updates on cooperation and efficiency. The principal finding is that, in trading settings with pure moral hazard and noisy ratings, if the per period profit margin of cooperating sellers is sufficiently high, a mechanism that does not publish every single rating it receives but rather only updates a trader's public reputation profile every k transactions with a summary statistic of a trader's most recent k ratings can induce higher average levels of cooperation and market efficiency than a mechanism that publishes all ratings as soon as they are posted. This paper derives expressions for calculating the optimal profile updating interval k, discusses the implications of this finding for existing systems, such as eBay, and proposes alternative reputation mechanism architectures that attain higher maximum efficiency than the, currently popular, reputation mechanisms that publish summaries of a trader's recent ratings.
This paper offers a systematic exploration of reputation mechanism design in trading environments with opportunistic sellers of commonly known cost and ability parameters, imperfect monitoring of a seller's actions, and two possible seller effort levels, one of which has no value to buyers. The objective of reputation mechanisms in such pure moral hazard settings is to induce sellers to exert high effort as often as possible. I study the impact of various mechanism parameters (such as the granularity of solicited feedback, the format of the public reputation profile, the policy regarding missing feedback, and the rules for admitting new sellers) on the resulting market efficiency. I find that maximum efficiency is bounded away from the hypothetical first- best case where sellers can credibly precommit to full cooperation by a factor that is related to the probability that cooperating sellers may receive "unfair" bad ratings. Furthermore, maximum efficiency is independent of the length of past history summarized in a seller's public reputation profile. I apply my framework to a simplified model of eBay's feedback mechanism and conclude that, in pure moral hazard settings, eBay's simple mechanism is capable of inducing the maximum theoretical efficiency independently of the number of recent ratings that are being summarized in a seller's profile. I derive optimal policies for dealing with missing feedback and easy online identity changes. Finally, I show that if the number of buyers is large, the results obtained in the monopoly case are also approximately valid in settings where multiple sellers of different reputations simultaneously offer auctions for identical goods.