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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.

With the proliferation of ideas submitted by users in firm-built online user innovation communities, community managers are faced with the problem of user idea overload. The purpose of this paper is to explore the influencing factors on the idea adoption to identify high quality ideas, and then propose a method to quickly filter high value ideas.

The authors collected more than 110,000 data submitted by Xiaomi community users and analyzed the factors affecting idea adoption using a multinomial logistic regression model. In addition, the authors also used BP neural network to predict the idea adoption process.

The empirical results show that idea semantics, number of likes, number of comments, number of related posts, the existence of pictures and self-presentation have positive impact on idea adoption, while idea length and idea timeliness had negative impact on idea adoption. In addition, this paper calculates the idea evaluation value through the idea adoption process predicted by neural network and the mean value of idea term frequency inverse document frequency (TF-IDF).

This empirical study expands the theoretical perspective of idea adoption research by using dual-process theory and enriches the research methods in the field of idea adoption research through the multinomial logistic regression method. Based on our findings, firms can quickly identify valuable ideas and effectively alleviate the information overload problem of online user innovation communities.

The purpose of this paper is to systematically review the existing research and summarize the value co-creation mechanism between enterprises and users in crowdsource-based open innovation (COI).

Crowdsourcing is an effective means for enterprises to launch open innovation. This paper will first introduce the concepts and forms of open innovation and crowdsourcing, and then define COI.

This paper will analyze the key parties in innovative tasks, value co-creation mechanism that creates ideas, the interaction of key participants and the process of integrating internal and external resources to realize open innovation.

In the end, this paper will put forward the theoretical framework for future studies on the development of COI from the perspective of value co-creation.

This thesis will first introduce the concepts and forms of open innovation and outsourcing and then define COI. Then it will systematically review the existing research studies and summarize the value co-creation mechanism between enterprises and users under COI model by analyzing the key parties in innovative tasks, value co-creation mechanism that creates ideas, the interaction mode of key participants and the process of enterprises integrating internal and external resources to realize open innovation.

Successful open innovation requires that many ideas be posted by a number of users and that the posted ideas be evaluated to find ideas of high quality. As such, successful open innovation community would have inherently information overload problem. The purpose of this paper is to mitigate the information problem by identifying potential idea launchers, so that they can pay attention to their ideas.

This research chose MyStarbucksIdea.com as a target innovation community where users freely share their ideas and comments. We extracted basic features from idea, comment and user information and added further features obtained from sentiment analysis on ideas and comments. Those features are used to develop classification models to identify potential idea launchers, using data mining techniques such as artificial neural network, decision tree and Bayesian network.

The results show that the number of ideas posted and the number of comments posted are the most significant among the features. And most of comment-related sentiment features found to be meaningful, while most of idea-related sentiment features are not in the prediction of idea launchers. In addition, this study show classification rules for the identification of potential idea launchers.

This study dealt with information overload problem in an open innovation context. A large volume of textual customer contents from an innovation community were examined and classification models to mitigate the problem were proposed using sentiment analysis and data mining techniques. Experimental results show that the proposed classification models can help the firm identify potential idea launchers for its efficient business innovation.

This study aims to explore how online reviews and users’ social network centrality interact to influence idea popularity in open innovation communities (OICs).

This study used Python to obtain data from the LEGO Innovation Community. In total, 285,849 reviews across 4,475 user designs between March 2019 and March 2021 were extracted to test this study’s hypotheses.

The ordinary least square regression analysis results show that review volume, review valence, review variance and review length all positively influence idea popularity. In addition, users’ in-degree centrality positively interacts with review valence, review variance and review length to influence idea popularity, while their out-degree centrality negatively interacts with such effects.

Drawing on the interactive marketing perspective, this study employs a large sample from the LEGO community and examines user design and idea popularity from a community member’s point of view. Moreover, this study is the first to confirm the role of online reviews and user network centrality in influencing idea popularity in OICs from a social network perspective. Furthermore, by integrating social network analysis and persuasion theories, this study confirms the interaction effects of review characteristics and users’ social network centrality on idea popularity.

This study’s results highlight that users should actively interact and share with reviewers their professional product design knowledge and/or the journey of their design to improve the volume of reviews on their user designs. Moreover, users could also draw more attention from other users by actively responding to heterogeneous reviews. In addition, users should be cautious with the number of people they follow and ensure that they improve their in-degree rather than out-degree centrality in their social networks.

This study integrates social network analysis and persuasion theories to explore the effects of online reviews and users’ centrality on idea popularity in OICs, a vital research issue that has been overlooked.

This study aims to demonstrate how destination management organizations can fruitfully harvest users’ ideas by facilitating an online idea contest to enhance value creation and innovation processes. The structure of the idea quality, contest-related factors and user-related factors are investigated in relation to the overall quality of the ideas submitted by users.

A total of 489 ideas were assessed based on the overall quality and effects of various factors. A structural model was tested to analyze the dimensions contributing to quality of the submissions that influence the overall idea quality. Furthermore, non-parametric tests were performed to reveal how specific user traits as well as contest-related traits relate to overall idea quality. Lastly, post-hoc analyses were performed to reveal if mean values differ among users who are grouped according to age, overall idea quality, place of residence and age at time of submission.

The study demonstrates that novelty, feasibility, relevance and elaboration explain overall idea quality. Only the age of the idea submitter exhibits an impact on the level of idea quality, wherein younger users tend to submit higher quality ideas. The areas of innovation significantly differ depending on the users’ place of residence and age. Moreover, none of the contest-related traits that were analyzed have an impact on the level of overall idea quality.

Marketing managers and destination management organizations are given a set of recommendations on how to facilitate internet-based participation tools, such as idea contests, to collect high-quality input from various user segments and, subsequently, to feed their value creation and innovation processes.

Within the field of tourism, the open tourism paradigm is relatively new. New insights into the role of online tools, and how they can be leveraged to harvest users’ ideas, as well as users’ capabilities for enhancing tourism firms’ value creation and innovation processes, are provided.

Online user innovation community (OUIC) has become a vital source for enterprises to obtain user innovation ideas and interact with users in new product development. However, most studies only focus on the relationship between users and ideas, often ignoring the influence of employees in the innovation platform. The purpose of this study is to explore the impact of employee behaviors on idea quality in OUIC.

In this paper, the authors collected sample data of open user innovation community – Idea Exchange – and then, the authors examined the direct roles of employee’s idea generation behaviors and idea promotion behaviors on idea quality and the moderating roles of social networks position and enthusiasm by using binary logistic regression model.

Results indicated that employee’s idea generation behaviors and idea promotion behaviors have a positive influence on users’ idea quality. Also, the social network position and characteristics show the moderation effect of employee behavior and idea quality.

This study is different from prior studies because it emphasizes the role of employees in the open source platform. The findings suggest that enterprises and platform managers pay more attention to the impact of employees and improve the quality of ideas and promote the development of OUIC.

Past research has demonstrated that industrial customers can, in effect, bring about product innovation among their suppliers. However, little seems to be known as to whether consumers are also potential inventors of new services. Presents results from an empirical study with the objective of exploring whether ordinary users can contribute novel service ideas regarding mobile telephony. An experimental approach was used to compare the characteristics of new services suggested by ordinary users with services suggested by professional developers. It was found that the service innovations suggested by the users were more creative and useful than those suggested by the professionals. On the other hand, the suggestions of the professionals were deemed easier to produce. Concludes with a discussion on the contributions and limitations of user involvement, wherein the organisational role of the users involved is discussed. Also makes a proposal regarding how to further investigate the potential of the user as a co‐worker in the innovation process.

The aim is to propose a conceptual framework consisting of research propositions concerning the key strategies required for the successful involvement of customers in the co‐creation of new technology‐based services.

The methodology involves a single case study from which data are derived and analyzed using the grounded theory methodology of “constant comparative analysis.” User‐generated ideas for future mobile phone services are collected from four user involvement projects and analyzed at several workshops attended by senior managers from telecommunications firms.

Seven key strategies are identified as being essential for successful user involvement in new product development. Each strategy is described and illustrated in relation to existing theory and presented as a research proposition.

The exploratory nature of the research means that the findings are tentative and need to be confirmed in other settings by other researchers, including quantitative large‐scale studies.

The results of the study provide management with guidelines for organizing successful user involvement projects with a market‐oriented approach.

Despite the increasing popularity of user involvement, little research has examined the conditions required for successful user involvement in new product development. This study makes an original contribution by proposing strategies critical for a successful outcome.

Virtual crowdsourcing initiatives, and in particular crowdsourcing competitions, have become a promising means of harnessing users’ creativity to help corporate innovation. To date, research has tended to focus on the outcome of the competition, i.e. on the creative solution. There is, however, a lack of understanding in such crowdsourcing environments of the creative process itself and the influence of social interaction on the platform during this process. The paper aims to discuss these issues.

The authors conducted a series of qualitative interviews with participants from a major European crowdsourcing platform. The platform acts as an intermediary between companies and firms, and has launched more than 370 idea competitions.

The results suggest that there are not only positive interactions going on between participants. Below the surface, there also appear destructive processes provoked by the fierce competition among the contestants for prizes and a position in the Top Innovator lists. Such destructive behavior includes bullying of successful contestants, excessive use of like-functions among befriended contestants, and mutual donation of prize money among in-group members.

Negative social interaction among contestants of crowdsourcing communities can potentially threaten the platform provider’s business model. Managers of crowdsourcing platforms should engage in the development of strong social norms explicitly disapproving destructive behavior.

This study is the first to investigate in detail the phase of idea generation on crowdsourcing platforms, and the nature and impact of social interactions among contestants.