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The purpose of this paper is to explore when, why and to what extent firms should intervene in firm-hosted idea-generation communities, and to develop a framework for firm-intervention.

A single case-study is conducted in a highly successful firm-hosted idea-generation community called Dell IdeaStorm, whereby the netnographic approach is applied.

The findings indicate that, overall, firm-participation is minimal and passive, and varies according to the three stages of the idea lifecycle in the community, such as ideation stage – here firm-participation is limited to acknowledgement of new ideas, checking for redundancy, managing search tool and profanity filtering; discussion and development stage – here firm-participation is more active by providing feedback and clarification when needed, troubleshooting, asking for additional input on an idea, etc.; and completion stage – here a firm intervenes to screen and select the most promising ideas for implementation and also provides status updates on ideas.

This study contributes by developing a new framework for firm-participation, which is useful for the early diagnosis of community issues in idea generation. The framework is also a tactical tool which can be used to guide community managers in selecting the correct moderation approach, depending on the specific stage in the idea lifecycle.

The purpose of this paper is to develop a framework for identifying the most promising user-generated ideas in user communities.

To develop the framework, each user-generated idea is first classified into a specific category depending on its source, such as market trend-, need-, solution- or mental ideation-based ideas. The degree of shared identity (shared agreement minus shared disagreement) based on user votes for each idea within the user community is then evaluated.

This study argues that unlike need-based user-generated ideas, trend-based ideas will not succeed in the marketplace even if they receive the highest ratings or the most votes from community members, and that hence such trend-based ideas should not be implemented. Moreover, solution- and mental ideation-based user ideas, even though they receive the lowest ratings or fewest votes from community members, are more likely to succeed in the marketplace, and thus, such ideas should not be discarded.

Community- and idea-level variables are combined to identify the most promising user-generated ideas for firms. This prevents overlooking the most promising ideas simply because their popularity is low within the user community. Moreover, this method prevents selecting the least promising ideas even though their popularity is high with the user community.

The purpose of this paper is to investigate two questions. First, are movie-based online community evaluations (CE) on par with film expert evaluations of new movies? Second, which group makes more reliable and accurate predictions of movie box office revenues: film reviewers or an online community?

Data were collected from a movie-based online community Fandango for a 16-month period and included all movies released during this time (373 movies). The authors compared film reviewers’ evaluations with the online CE during the first eight weeks of the movie’s release.

The study finds that community members evaluate movies differently than film reviewers. The results also reveal that CE have more predictive power than film reviewers’ evaluations, especially during the opening week of a movie.

The investigated online community is based in the USA, hence the findings are limited to this geographic context.

The main implication is that film studios and movie-goers can rely more on CE than film reviewers’ evaluation for decision making. Online CE can help film studios in negotiating with distributors, theatre owners for the number of screens. Also, community reviews rather than film reviewers’ reviews are looked upon by future movie-goers for movie choice decisions.

The study makes an original contribution to the motion picture performance research as well as to the growing research on online consumer communities by demonstrating the predictive potential of online communities with regards to evaluations of new movies.