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We believe that it is an important and difficult challenge to design technical features of online communities and seed their social practices in a way that generates ongoing contributions from a larger fraction of the participants.

Economists and political scientists have observed that across a wide range of settings, people contribute less than the optimal amount of public goods and consume more than their fair share of common pool resources, although the antisocial behavior is considerably less than theories based on pure short-term self-interest would predict (see Ledyard, 1995 for a review).

Social psychologists have identified an analogous phenomenon called social loafing. People exert less effort on a collective task than they do on a comparable individual task (see Karau & Williams, 1993 for a review).

Social loafing, or free riding, is the robust phenomenon that occurs when people work less hard to achieve some goal when they think they are working jointly with others than when they think they are working by themselves. Karau and Williams' (1993) collective-effort model is a type of utility theory that claims that people work hard when they think their effort will help them achieve outcomes they value. Working in a group can influence how hard people work because it can change their perception of the importance of their contribution to achieving a specified level of performance, their likelihood of reaching the goal, and the value they place on the outcomes they gain by their efforts

The collective effort model identifies conditions under which people will socially loaf less. These include, among others: (a) believing that their effort is important to the group's performance, (b) believing that their contributions to the group are identifiable, and (c) liking the group they are working with.

The collective effort model posits that people will socially loaf less and contribute to a group more, the more they see their contribution as important to the group (Karau & Williams, 1993). If they believe that their contributions are redundant with those that others in the group can provide, then there is little reason to contribute, because their contributions have little likelihood of influencing the group. Conversely, if they think they are unique, they should be more motivated to contribute,

The collective effort model posits that people will socially loaf less and contribute more to a group the more they like it (Karau & Williams, 1993). By doing so, they increase their own utility by benefiting the group. In contrast, they do not receive the same benefit if they contribute to groups they dislike.

People tend to like others who are similar to themselves (Byrne, 1997; Byrne & Griffith, 1973) and to dislike groups composed of dissimilar members (Williams & O'Reilly, 1998). In Experiment 1, we manipulated subjects' liking for their discussion group by populating the group with others who had either similar or dissimilar tastes in movies.

Consistent with Hypothesis 1, subjects posted more messages in the uniqueness condition, when they were given personalized information about how their knowledge of movies differed from others (See Table 1: z=2.88, p<.004). However, Hypothesis 2 was disconfirmed. Subjects posted fewer messages when conversing in groups constructed so that members had similar tastes in movies than in groups with heterogeneous members (z=-2.45, p<.05).

Both posting and rating data show that people contributed more when they were made to see themselves as having unique information to contribute. In retrospect, the finding that subjects posted more to the conversation forum when they were least similar to those they were talking to may also reflect the influence of uniqueness.

When individuals rate movies, they benefit the community as a whole by increasing the accuracy of recommendations that others receive. However, this benefit to the community may not be visible to members, because they do not have the data to see the correlation between their ratings and the accuracy of recommendations for others. Therefore, making explicit the benefit that the community receives from their ratings should increase their ratings. Hypothesis 3b: MovieLens users will rate more movies when the benefit they provide to the community from doing so is made salient.

The benefit manipulation contained four conditions: no benefit, only benefit to self, only benefit to others, and benefit to both self and others. Participants who received the self-benefit manipulation received a message that said, "Rating more movies helps you! The more ratings you provide, the easier it is for MovieLens to identify people with similar taste to yours, and thus make accurate recommendations for you." Participants who received the other-benefit manipulation received a message that said, "Rating more movies helps the MovieLens community! The more ratings you provide, the more information we have about each movie and the easier it is to make accurate recommendations for other people." Participants in the both-self-and-other-benefit condition received a combination of these messages, but those in the no-benefit condition received neither.

Of the 830 participants who received email, 397 (47.8%) members logged in and rated at least one movie. Descriptive analysis including all 830 participants showed that they rated an average of 19.26 movies during the week following the invitation, far higher than the 5.4 movies per week they had rated in the previous six months.

articipants who logged in during the experiment rated on average 39.7 movies, far higher than the 9.1 ratings made by individuals from a matched control group who logged in during the week of the experiment.

The results of this experiment confirm what telemarketers know: Email messages can motivate people in an online community simply by reminding them of an opportunity to contribute. More interestingly, the content of the message made a difference, partially in line with the collective effort model. Making members of the community feel unique encouraged them to contribute more in general, and especially to contribute in the domain where they were unique.

Previous research has shown that when people are intrinsically motivated to perform some behavior, the promise of extrinsic rewards, such as money or grades, reduces their intrinsic interest in it (Thompson, Meriac, & Cope, 2002). As a result, they are less likely to perform the behavior in the absence of the reward, compared to those who were never offered a reward.

Goal setting theory, a robust theory of motivation in social psychology, has shown that assigning people challenging, specific goals causes them to achieve more (Locke & Latham, 1990, 2002).

Hundreds of studies with over 40,000 subjects have shown that specific, challenging goals stimulate higher achievement than easy or "do your best" goals (Locke & Latham, 1990). High-challenge assigned goals energize performance in three ways (Bandura, 1993). First, they lead people to set higher personal goals, in turn increasing their effort. Second, assigned goals enhance self-efficacy, or belief in one's own ability to complete a task (Bandura, 1993). Third, achieving an assigned goal leads to task satisfaction, which enhances both self-efficacy and commitment to future goals, resulting in an upward performance spiral.

The theory claims that difficult, specific, context-appropriate, and immediate goals, rather than long-term goals, motivate people most, and that they do so especially in tasks that are simple and non-interdependent and give them feedback on their performance against the goal.

Although most research on assigned goals has assigned them only to individuals, assigning goals to groups shows the same motivating effects (see Weldon & Weingart, 1993 for an overview). The collective effort model (Karau & Williams, 1993) predicts that individual goals and feedback will be more motivating than group goals, because in a group setting people can believe that their contribution is partially redundant and that if they shirk, others can take up the slack. Although some studies have found that group goals are more motivating than individual goals, these findings are reversed as group size increases beyond 3-5 members (Streit, 1996).

Goal Specificity Non-specific goal condition subjects were told to "do your best" to rate movies. Their message said, "[You/The Explorers] have a goal of doing [your/their] best to rate additional movies over the next seven days." In the specific-goal condition, subjects were assigned a specific number of movies to rate. We asked individual-goal-condition subjects to either rate 8, 16, 32 or 64 movies in a week, and subjects in the 10-member "Explorers" group to rate either 80, 160, 320 or 640 movies in a week. We set eight ratings per week as a baseline goal based on subjects' mean weekly contribution in the past.

Subjects who received specific goals were marginally more likely to log in than those who received do-your-best goals (z=1.73, p=.08).

Hypothesis 6, which predicted that members given specific numeric goals would rate more than those given do-your-best goals, was supported. Subjects rated 27% more movies when given one of the specific goals than the non-specific do-your-best goals (z=.2.87, p<.01). Moreover, the marginally significant group goal specific goal interaction (z=1.67, p<.10) indicates specific goals had a larger effect in the individual-goal condition than in the group condition.

Hypothesis 7, which predicted members given individual goals would rate more movies than those with group goals, was disconfirmed. Subjects in the individual-goal condition rated 42% of the movies they rated in group-goal condition (z=-2.43, p<.02).

Although, based on the collective effort model, we had expected that individual goals would be more effective than group goals in stimulating contribution, we found the reverse. Two alternatives may explain this reversal. First, naming the group "Explorers" may have motivated greater effort by making members identify with this in-group, to the exclusion of unnamed out-groups (Kane, Argote, & Levine, 2005; Tajfel & Turner, 1986). Second, the presence of both individual and group level feedback in the group condition may have resulted in social facilitation (Zajonc, 1965) rather than social loafing.

One key insight from the collective effort model is that people will be more likely to contribute to a group task if they think their contribution will not duplicate what others can provide and is thus needed for accomplishing the group's goal. Many online communities provide feedback on the number or assessed quality of their contributions, like the "top reviewer" designations given to some contributors on the www.epinions.com website. However, we know of no online community that provides feedback to contributors about the uniqueness of their contributions.