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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.

The paper presents the pros and cons of crowdsourcing competitions and highlights the importance of strategy and collaborative efforts. The study identifies the key stakeholders of crowdsourcing and its critical elements (7Ps) that need to be mapped and managed efficiently for obtaining innovative solutions.

The paper draws its insights from the explorative research conducted over a two-year period (2016-18). Qualitative interviews held with competition organizers, participants and innovation intermediaries (Innocentive, Nine Sigma, Skild) provided the primary data. Secondary data came from literature survey and the study of archival documents and competition websites. The study was conducted as a part of doctoral research.

Crowdsourcing competitions can help organizations to discover innovative solutions by tapping the power of collective intelligence. However, they need to envision and execute these collaborative initiatives strategically and synergistically. Proper design, managerial buy-in and orchestrated efforts by the triumvirate ‘Seekers, solvers and supporters’ are critical to derive the desired outcomes.

These findings are the resultant outcomes of an exploratory research. Further investigation can help companies to identify the relative importance of the critical elements identified in the study. Future research on the best practices can amplify the prospects of finding innovative solutions through crowdsourcing competitions.

Crowdsourcing competitions cannot be used impulsively and indiscriminately. Managers have to carefully align the motive and incentive of different actors. Attention to design and the critical factors identified in the study can enhance the prospects of getting qualitative and innovative submissions from the crowd.

Crowdsourcing competitions have great potential to find innovative solutions for many stubborn global problems. When designed and driven rightly, it can expand the solution mix and accelerate the discovery process.

The paper highlights the need to converge the efforts for diverse stakeholder in crowdsourcing competitions. There are actionable insights for managers in form of 7 Ps- Purpose, Problem, Prize, Platform, Promotion and Partners. When aligned effectively, it can yield innovation dividends to all. By focusing on these vital factors, companies can fine-tune their crowdsourcing strategy and make the initiative more engaging and create value for all the actors.

The research study intends to gain a better understanding of members' behaviors in the context of crowdsourcing contests. The authors examined the key factors that can motivate or discourage contributing to a team and within the community.

The authors conducted 21 semi-structured interviews with Kaggle.com members and analyzed the data to capture individual members' contributions and emerging determinants that play a role during this process. The authors adopted a qualitative approach and used standard thematic coding techniques to analyze the data.

The analysis revealed two processes underlying contribution to the team and community and the decision-making involved in each. Accordingly, a set of key factors affecting each process were identified. Using Holbrook's (2006) typology of value creation, these factors were classified into four types, namely extrinsic and self-oriented (economic value), extrinsic and other-oriented (social value), intrinsic and self-oriented (hedonic value), and intrinsic and other-oriented (altruistic value). Three propositions were developed, which can be tested in future research.

The study has a few limitations, which point to areas for future research on this topic. First, the authors only assessed the behaviors of individuals who use the Kaggle platform. Second, the findings of this study may not be generalizable to other crowdsourcing platforms such as Amazon Mechanical Turk, where there is no competition, and participants cannot meaningfully contribute to the community. Third, the authors collected data from a limited (yet knowledgeable) number of interviewees. It would be useful to use bigger sample sizes to assess other possible factors that did not emerge from our analysis. Finally, the authors presented a set of propositions for individuals' contributory behavior in crowdsourcing contest platforms but did not empirically test them. Future research is necessary to validate these hypotheses, for instance, by using quantitative methods (e.g. surveys or experiments).

The authors offer recommendations for implementing appropriate mechanisms for contribution to crowdsourcing contests and platforms. Practitioners should design architectures to minimize the effect of factors that reduce the likelihood of contributions and maximize the factors that increase contribution in order to manage the tension of simultaneously encouraging contribution and competition.

The research study makes key theoretical contributions to research. First, the results of this study help explain the individuals' contributory behavior in crowdsourcing contests from two aspects: joining and selecting a team and content contribution to the community. Second, the findings of this study suggest a revised and extended model of value co-creation, one that integrates this study’s findings with those of Nov et al. (2009), Lakhani and Wolf (2005), Wasko and Faraj (2000), Chen et al. (2018), Hahn et al. (2008), Dholakia et al. (2004) and Teichmann et al. (2015). Third, using direct accounts collected through first-hand interviews with crowdsourcing contest members, this study provides an in-depth understanding of individuals' contributory behavior. Methodologically, this authors’ approach was distinct from common approaches used in this research domain that used secondary datasets (e.g. the content of forum discussions, survey data) (e.g. see Lakhani and Wolf, 2005; Nov et al., 2009) and quantitative techniques for analyzing collaboration and contribution behavior.

The authors advance the broad field of crowdsourcing by extending the literature on value creation in the online community, particularly as it relates to the individual participants. The study advances the theoretical understanding of contribution in crowdsourcing contests by focusing on the members' point of view, which reveals both the determinants and the process for joining teams during crowdsourcing contests as well as the determinants of contribution to the content distributed in the community.

Often, it is assumed that teams are better at solving problems than individuals working independently. However, recent work in engineering, design and psychology contradicts this assumption. This study aims to examine the behavior of teams engaged in data science competitions. Crowdsourced competitions have seen increased use for software development and data science, and platforms often encourage teamwork between participants.

We specifically examine the teams participating in data science competitions hosted by Kaggle. We analyze the data provided by Kaggle to compare the effect of team size and interaction frequency on team performance. We also contextualize these results through a semantic analysis.

This work demonstrates that groups of individuals working independently may outperform interacting teams on average, but that small, interacting teams are more likely to win competitions. The semantic analysis revealed differences in forum participation, verb usage and pronoun usage when comparing top- and bottom-performing teams.

These results reveal a perplexing tension that must be explored further: true teams may experience better performance with higher cohesion, but nominal teams may perform even better on average with essentially no cohesion. Limitations of this research include not factoring in team member experience level and reliance on extant data.

These results are potentially of use to designers of crowdsourced data science competitions as well as managers and contributors to distributed software development projects.

Purpose: The insurance business is confronted with coordination difficulties that necessitate a high level of mobility, flexibility, and the capacity to analyse heterogeneous, location-dependent data from different sources and qualities. Recent innovations in emerging technologies have given the insurance industry new organisational options. When coupled with data analytics, crowdsourcing in the insurance industry facilitates solving complex issues with the wisdom of crowds. The notion of incorporating crowdsourcing and big data into the mainstream activities of insurance management is developed in this article, as are the ramifications and gains of collective intelligence achieved by Crowdsourcing and the added value of crowdsourcing insurance activities.

Design/methodology/approach: This chapter is a conceptual work that builds on relevant literature.

Findings: This chapter analyses what insurance industry managers should consider when coordinating crowdsourced activities and how they may benefit from collective intelligence combined with data analytics in terms of efficient and real-time response management for the insurance industry. Furthermore, it is demonstrated how they may use crowdsourcing to exploit information and benefit from invoking additional resources and eliminating the institutional voids present in the industry.

Practical implications: Exemplary applications that take advantage of crowdsourcing and data analytics would help the insurance sector respond flexibly, efficiently, and effectively in real time.

Originality/value: This chapter offers new collaborative ways to enhance the decision-making of insurance industry managers. The relevance of overcoming institutional voids is expanded, and repercussions from the given framework are suggested using data analytics.

The purpose of this paper is to examine the efficacy of the Japanese ten-item personality inventory (TIPI-J), a short version of the big five (BF) questionnaire, on crowdsourcing. The BF traits are indicators of personality and are said to be an effective predictor of study performance in various occupations. BF can be used in crowdsourcing to predict crowd workers’ performance; however, it will be difficult to use in practice for two reasons like the time-and-effort issue and the bias issue. In this study, an empirical analysis is conducted on crowdsourcing to examine if TIPI-J can solve those issues.

To investigate the issues, two tasks are posted on a crowdsourcing provider. Both TIPI-J and full version BF are conducted before and after selecting crowd workers. Structural validity and convergence validity are tested with correlation analysis between before (TIPI-J) and after (full version BF) data to examine the bias issue. Additionally, those correlations are compared with previous study and significances are examined.

The correlations in “conscientiousness” is 0.45-0.50, respectively, compared with a previous study, those two correlations did not show significance. This indicates that no clear bias exists.

This is the first research to investigate the efficacy of TIPI-J on crowdsourcing and showed that TIPI-J can be a useful tool for predicting crowd workers’ performance and thus it can help to select appropriate crowd workers.

Existing studies on crowdsourcing have focused on analyzing isolated contributions by individual participants and thus collaboration dynamics among them are under-investigated. The value of implementing crowdsourcing in problem solving lies in the aggregation of wisdom from a crowd. This study examines how marginality affects collaboration in crowdsourcing.

With population level data collected from a global crowdsourcing community (openideo.com), this study applied social network analysis and in particular bipartite exponential random graph modeling (ERGM) to examine how individual level marginality variables (measured as the degree of being located at the margin) affect the team formation in collaboration crowdsourcing.

Significant effects of marginality are attributed to collaboration skills, number of projects won, community tenure and geolocation. Marginality effects remain significant after controlling for individual level and team level attributes. However, marginality alone cannot explain collaboration dynamics. Participants with leadership experience or more winning ideas are also more likely to be selected as team members.

The core contribution this research makes is the conceptualization and definition of marginality as a mechanism in influencing collaborative crowdsourcing. This study conceptualizes marginality as a multidimensional concept and empirically examines its effect on team collaboration, connecting the literature on crowdsourcing to online collaboration.

The purpose of this paper is to study the participation behaviors in the context of crowdsourcing projects from the perspective of gamification.

This paper first proposed a model to depict the effect of four categories of game elements on three types of motivation based upon several motivation theories, which may, in turn, influence user participation. Then, 5 × 2 between-subject Web experiments were designed for collecting data and validating this model.

Game elements which provide participants with rewards and recognitions or remind participants of the completion progress of their tasks may positively influence the extrinsic motivation, whereas game elements which can help create a fantasy scene may strengthen intrinsic motivation. Besides, recognition-kind and progress-kind game elements may trigger the internalization of extrinsic motivation. In addition, when a task is of high complexity, the effects from game elements on extrinsic motivation and intrinsic motivation will be less prominent, whereas the internalization of extrinsic motivation may benefit from the increase of task complexity.

This study may uncover the motivation mechanism of several different kinds of game elements, which may help to find which game elements are more effective in enhancing engagement and participation in crowdsourcing projects. Besides, as task complexity is used as a moderator, one may be able to identify whether task complexity is able to influence the effects from game elements on motivations. Last, but not the least, this study will indicate the interrelationship between game elements, individual motivation and user participation, which can be adapted by other scholars.

This paper analyzes how the distribution and structure of employees' attention influence idea survival in an organizational internal crowdsourcing session.

Data from an online internal crowdsourcing session carried out within a multinational company with headquarters in Sweden were used to explore how idea attention influenced idea survival.

Our findings indicate that the positive relationship between attention allocation and idea survival is mediated by idea appreciation, i.e. positive comments and suggestions that employees provide in response to ideas. In addition, we find that competition for attention negatively moderates the relationship between idea attention and positive comments. Finally, our results indicate that ideas are more likely to survive if they are submitted earlier in the crowdsourcing process and when the elapsed time since previously posted ideas in the session is longer.

This study provides organizers of internal crowdsourcing sessions with new insights about factors influencing idea survival and about potential systematic biases in idea selection due to timing and competition between ideas.

This paper contributes to the literature highlighting the relevance of attention-based theory in the context of crowd-based creativity and innovation management.

In crowdsourcing contests, the capabilities and performance of individual workers (solvers) determine whether seeker firms can obtain satisfactory solutions from the platform. It is noted that solvers may learn such skills in crowdsourcing from doing (experiential learning) or observing (vicarious learning). However, it remains unclear if such learning can be materialized into improved performance considering the unique settings of crowdsourcing contests. The study aims to understand how experiential learning and vicarious learning enhance solver performance and under what conditions.

The model was tested using survey and archival data from 261 solvers on a large contest platform in China.

Results support the premise that experiential learning and vicarious learning separately and jointly enhance solver performance. Moreover, perceived task uncertainty strengthens the effect of vicarious learning but weakens the effect of experiential learning, whereas perceived competition uncertainty weakens the effect of vicarious learning.

The current study enriches the understanding of the impacts of experiential learning and vicarious learning and offers a more nuanced understanding of the conditions under which solvers can reap the performance benefits from learning in crowdsourcing contests. The study also provides practical insights into enhancing solver performance under perceived task uncertainty and perceived competition uncertainty.