Many among the largest WEB2.0 communities are built upon the concept of Users Generated Contents (UGC), a business model in which the members of the community are also the creators of the contents/resources the community rely on. Notable examples of this kind are the cooperative encyclopedia Wikipedia.org and the video-sharing service YouTube.com.
This type of systems sometimes provides means to cooperatively evaluate and classify the resources the users share, but the vast majority of them lack an accurate ranking system for their most proficient members and none of them includes effective rewarding mechanisms.

Although rough, the current technology seems to be sufficient to confer to some cooperative web communities a good success, which for the most part appears to be due to their core-members passion and commitment. Nevertheless, deficiencies like the ones described usually involve that over time only a small amount of the members keep committed and willing to produce resources of a certain quality. This tendency leads to the situation in which, although the community grows in terms of numbers, in proportion its value decreases.

Providing cooperative communities with effective rewarding systems could solve the problem. Unfortunately this approach rarely and loosely has been implemented so far. The reason is that the task involves several not trivial side problems. The biggest is the necessity of an advanced ranking mechanism to evaluate the members of the community. Such a method should consider complex factors like competence and commitment, but at the same time it should be easily understandable and shared by the members of the community. Also, in order to make sure that the members agree with the rating system it must involve their collaboration, which implies finding ways to prevent misuse.

This is not an easy task, nonetheless considering the fast diffusion of web2.0 communities and the new emerging Enterprise2.0 communities the problem is no longer to be underestimated.

Digital communities should start thinking of new means to rise their quality standards in order to keep effective and successful over time.

One solution to the problem could be the adoption of an RCE business model (Reputation Community Evaluation). The RCE model exploits users reputation, rather than only competence and/or commitment, as evaluation parameter, and implements ways to use reputation as a digital currency in order to promote an healthy community growth.

The assumption is that communities equipped with a reputation system can benefit of a raise in their value. Using an RCE business model this rise in value can be then capitalized and redistributed among its most proficient members and the system provider.

To truly understand the real value of reputation we need to set our minds to think of reputation as a monetary capital, as a matter of fact although by personal experience we all have an idea of what reputation means, this idea is often partial and doesn’t cover all the complexity and potential that reputation involves.

Reputation is about what you do, what you say, and above all what other people say about you. This latter aspect implies reputation to be partially biased by people specific points of view. Reputation evaluation is therefore, by the very nature of reputation itself, a very complicated task.

There are many websites with reputation systems of some sort. The eBay system is undoubtedly the biggest and best known and is also a clear example of the economical implications that a reputation system can have in an online community.

Reputation itself is in economy addressed as a social currency, in the sense that it has an INTANGIBLE VALUE yet it is CONCRETELY SPENDABLE and therefore can be exploited at the level of every other type of economical capital. Even more, reputation has properties beyond the traditional capitals, it is: secure and transitive .

Secure because reputations cannot be subverted, and the source of reputation assertions can always be traced, especially in online systems. Transitive because, as an example, if A trusts B, and B trusts C, then it could be determined that A trusts C.

The economical implications of reputation in online systems have been in the last few years evaluated by a discrete number of theoretical studies and a fairly large number of empirical studies which for the most part involved the study of the effects of eBayʼs reputation system on sales.

These studies demonstrate that a reputation system is able to affect the perception of the quality of the goods. An item associated with the reputation of a seller is more valuable than the same item not “tagged” with any reputation information. So it is clear that a reputation system increases not only the value of single item but also of the system as a whole. For instance, eBay without any reputation system would be much less valuable than the current eBay and likely less popular.

For this reason automated reputation mechanisms should not be designed without regards to the “economy of reputation”, but through a careful consideration of both the computational and the economical aspects.

There are also several issues that any robust reputation system needs to solve to be effective. Following some among the most important.

The eBay reputation system proves that if buyers are uncertain about seller’s trustworthiness, they will reward better seller reputations by raising their offers.  Another aspect we must consider is that if it is costly to maintain a reputation for high quality (at least in time and effort), then a good reputation needs to be rewarded by the cost of building one. Similarly a bad reputation or a decline in reputation should incur a loss that exceeds the benefit from opportunistic behavior.

Reputation systems motivate people in ways monetary economies do. Any community that is equipped with a reputation system can truly benefit of a raise in its value only if the associated business model is capable to redistribute part of the extra value to the most valuable members of the community and the system provider.

In some rating based reputation systems it is to most raters’ perceived advantage that everyone agree with the rater. This is how chain letters, Amway and Ponzi schemes work. They establish a system in which customers are motivated to recruit other customers.

As an example: If a vendor offered to discount past purchases if enough future customers buy the same product, it would be hard to get honest ratings for that vendor. All the buyers, in order to foster new selling and get the discount would rate the vendor very high.

Among the simpler attacks that a reputation system can suffer the simplest yet very spread and dangerous, is called shilling. This term is often used to refer to submitting fake bids in an auction, but it can be considered in a broader context of submitting fake or misleading ratings, often exploiting identity forgery.

In particular, a person might submit positive ratings for one of her friends (positive shilling) or negative ratings for her competition (negative shilling). Either of these ideas introduces more subtle attacks, such as negatively rating a friend or positively rating a competitor to trick others into believing that competitors are trying to cheat.

Reputation-based trust must have some method to bootstrap the system ^[18]. The process of building a profile for new users is an ongoing process throughout the entire lifetime of the system. To efficiently bootstrap a reputation system it is vital to leverage on the reasons that make people willing to use the application in which the reputation system is placed. The real reasons often differ from the declared ones, and need ways to be investigated.

Several motivations lead people to contribute to virtual communities and several researchers have investigated the matter.

Peter Kollock,  in his book “Communities in Cyberspace” outlines three motivations that are interesting because do not rely on altruistic behavior of the contributor: anticipated reciprocity, increased recognition, and sense of efficacy.

“Anticipated Reciprocity

Active participants in online communities get more responses faster to questions than unknown participants. A person is motivated to contribute valuable information to the group in the expectation that one will receive useful help and information in return.

Increased Recognition

Recognition is important to online contributors such that, in general, it is a key ingredient for encouraging community participation and reputation development.

Sense of Efficacy

Individuals may contribute because the act results in a sense of efficacy, that is, a sense that they have had some positive impact on the group and, sometimes, on their own self-image as an efficacious person. “

Another motivation, implicit in the ones above, is Sense of Community.

People, in general, are fairly social beings and it is motivating to many people to receive direct responses to whether one’s contribution was helpful or not.

In contrast to participants many people who join virtual community spaces remains lurkers and do not contribute.

There are several reasons why people choose not to participate online: having nothing to say/share, getting what they needed without having to participate actively, thinking that they were being helpful by not posting, wanting to learn more about the community before diving in, not being able to use the software because of poor usability and not liking the dynamics that they observed within the group ^[15].

The work of Kollock, even if in one sense is very exhaustive on the other hand has the limitation of being mostly based on people opinions and studies based on surveys. Unfortunately often people donʼt say all the truth.

A more recent work, among others, which nicely explores the matter from a more practical point of view is “Six degrees of reputation: The use and abuse of online review and recommendation systems” by David Shay and Trevor John Pinch, 2006 ^[4].

In this work Shay and Pinch, using as an example the famous e-marketplace Amazon.com demonstrates in concrete that reasons of online participations are mostly bound to economical factor rather than people good will. Shay and Pinch choose Amazon since they sell predominantly books, that are a particular kind of goods for which reviews are particularly powerful because they help establish the meaning of the artifacts in question.

Moreover at Amazon reviewers are invoked as a legitimate authority even though the only thing required to be be a reviewer is participation.
Shay and Pinch found out that Amazon reviewers participate:

“A. To share their opinion with the community,

B. To build an identity as a reviewer,

C. To get a job as a professional book reviewer,

D. Empowerment of seeing their name and review on a Web site and take pride in their ability to ‘publish’,

E. To legitimately (or not) promote a certain item,

F. Slur the competition attack others via posting negative reviews,

G. Self Promotion: “Reviews” from friends, paid professionals, author,

H. Review plagiarism (to promote or support the sales of a specific item, agenda or opinion),

I. To increase credibility and to build their identity,

J. Socket Puppets: Posting the same review multiple times for the same item, under different reviewer names,

K. To simply for free advertisements or spamming,

I. Promotion of political agendas.”

So in many cases people contribute in the perspective of getting a direct or an indirect economical advantage. This not always turn out to be a security or a resource-reliability problem but often it does.

Also a special mention must be made regarding Enterprise 2.0.

As a matter of fact Enterprise 2.0 collaborative/cooperative systems are often thought to be WEB 2.0 systems, especially from the business-model point of view. This usually isn’t the case.

As an example in enterprise communities, differently from the WEB ones, sharing one’s knowledge with others can be considered a bad investment. In a working environment having a knowledge that others donʼt have makes an individual unique and therefore very valuable. Obviously the inverse applies too.

Moreover sharing can involve a significant amount of time from contributors, prolongating their working hours over the regular duties, usually without any additional gain. The end result is that Enterprise 2.0 cooperative systems can comport even losses for their contributors, legitimating thus reluctance to participation.

In conclusion the key for a successful digital cooperative community is to motivate participants. In this respect economical factors (implicit or explicit) can be very relevant.

To solve the problem of providing incentives for cooperative communities we can leverage on the “economy of reputation”, driving thus our community from stagnation to innovation.

The RCE model is a research project that adopting ad-hoc algorithms and business strategies aims to solve the problems that reputation evaluation involves. This way reputation can be used as a digital currency to safely bond usersʼ reputations to a self efficient rewarding mechanism, propelling communities’ growth and standards of quality.

Enterprise 2.0 communities can benefit the most from the RCE model adoption. A rewarding mechanism tied to usersʼ reputation is a fair mean to counterbalance the disadvantages. Even more, an RCE system can be a very effective mean to empower employees making more visible the ones with an higher reputation.

Also reputation is a very effective metric for automatically, safely and precisely detecting through Social Network Analysis techniques those people with certain skills or with implicit leadership over the others.

This way the Enterprise 2.0 system turns from an enemy to a valuable alley in promoting one’s career and a more effective tool for the company.

In this article many details are omitted. The goal was to give a little glimpse about the opportunities that the adoption of a reputation-based model such as RCE can provide.