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To obtain optimal deliverables, more and more crowdsourcing platforms allow contest teams to submit tentative solutions and update scores/rankings on public leaderboards. Such feedback-seeking behavior for progress benchmarking pertains to the team representation activity of boundary spanning. The literature on virtual team performance primarily focuses on team characteristics, among which network closure is generally considered a positive factor. This study further examines how boundary spanning helps mitigate the negative impact of network closure.

This study collected data of 9,793 teams in 246 contests from Kaggle.com. Negative binomial regression modeling and linear regression modeling are employed to investigate the relationships among network closure, boundary spanning and team performance in crowdsourcing contests.

Whereas network closure turns out to be a negative asset for virtual teams to seek platform feedback, boundary spanning mitigates its impact on team performance. On top of such a partial mediation, boundary spanning experience and previous contest performance serve as potential moderators.

The findings offer helpful implications for researchers and practitioners on how to break network closure and encourage boundary spanning with the establishment of facilitating structures in crowdsourcing contests.

The study advances the understanding of theoretical relationships among network closure, boundary spanning and team performance in crowdsourcing contests.

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.

The rapid development of Web 2.0 and social media enables the rise of crowdsourcing. Crowdsourcing contest is a typical case of crowdsourcing and has been adopted by many organisations for business solution and decision making. From a participant's perspective, it is interesting to explore what motivates people to participate in crowdsourcing contest. The purpose of this paper is to investigate the category of motivation based on self-determination theory and synthesises various motivation factors in crowdsourcing contest. Meanwhile, perceived motivational affordances and task granularity are also examined as the moderate constructs.

The paper builds a conceptual model to illustrate the relationships between various motivations (extrinsic and intrinsic) and participation effort under the moderating of perceived motivational affordances and task granularity. An empirical study is conducted to test the research model by surveying the Chinese participants of crowdsourcing contest.

The results show that various motivations might play different roles in relating to participation effort expended in the crowdsourcing contest. Moreover, task granularity may positively moderate the relationship between external motivation and participation effort. The results also show that supporting of a participant's perceived motivational affordances might strengthen the relationship between the individual's motivation with an internal focus (intrinsic, integrated, identified and introjected motivation) and participation effort.

Overall, the research has some conceptual and theoretical implications to the literature. This study synthesises various motivation factors identified by previous studies in crowdsourcing projects or communities as a form of motivation spectrum, namely external, introjected, identified, integrated and intrinsic motivation, which contributes to the motivation literatures. Meanwhile, the findings indicate that various motivations might play different roles in relating to participation effort expended in the crowdsourcing contest. Also, the study theoretically extends the crowdsourcing participation research to incorporate the effects of perceived motivational affordances in crowdsourcing contest. In addition, the study may yield some practical implications for sponsors, managers and designers in crowdsourcing contest.

As the application of crowdsourcing contests grows, leveraging the participation of superstars (i.e. solvers who have outstanding performance records in a crowdsourcing platform) becomes an emergent approach for managers to solve crowdsourced problems. Although much is known about superstars’ performance implications, it remains unclear whether and how their participation affects the size of a contest crowd for a crowdsourcing contest. Based on social contagion theory, this paper aims to examine the impact of superstars’ participation on the crowd size and studies how this impact varies across solvers with different heterogeneity in terms of skills, exposure and cultural proximity with superstars in crowdsourcing contests.

This paper uses secondary data from one crowdsourcing platform that includes 6,587 innovation contests to examine superstars’ main and contextual effects on the crowd size of a contest.

Our results reveal that superstars’ participation positively affects the crowd size of a contest in general. This finding suggests that social contagion is a fundamental mechanism underlying crowd formation in crowdsourcing contests. Our results also indicate that in contests that involve multiple superstars, superstars’ effect on crowd size becomes negative when we simultaneously consider other solvers’ heterogeneity in terms of skills, exposure and cultural background, and this negative effect will be intensified by increases in the skill gap, extent of exposure and cultural proximity between superstars and other solvers in the same contest.

Our research enhances the understanding of the influence of superstars and the mechanism underlying the emergence of contest crowds in crowdsourcing contests and contributes knowledge to better understand social contagion in a competitive setting. The results are meaningful for sourcing managers and platform supervisors to design contests and supervise crowd size in crowdsourcing contests.

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.

The online crowdsourcing has been widely applied in the practice. The purpose of this paper is to investigate the all-pay auction contest in crowdsourcing, wherein a seeker posts a task online and the solvers decide whether to participate in the contest and in what extent to spend efforts on their submissions.

The authors specifically consider two classic contest formats: simultaneous contest and sequential contest, depending on whether the solver can observe the prior solvers’ submissions before making her own effort investment decision or not. They derive both seeker’s and solver’s equilibrium decisions and payoffs under different contest formats, and show that they vary significantly according to the number and the average skill level of solvers.

The results show that a solver would always invest more on her submission under simultaneous contest than under sequential contest, as she cannot confirm how other solvers’ submissions would be. This subsequently intensifies the market competition and brings down a solver’s average payoff under simultaneous contest. Although the simultaneous contest gives rise to a higher expected highest quality of all submissions, it also requires the seeker to spend more search cost to identify the best submission. Therefore, when the number of solvers is high or the average skill level is low, the seeker prefers sequential contest to simultaneous contest. The results also show an analogous preference over two formats for the platform.

This paper investigates two formats of all-pay auction contest in crowdsourcing and evaluates them from the perspective of solvers, seekers and platforms, respectively. The research offers many interesting insights which do not only explain the incentive mechanisms for solvers under different contest formats, but also make meaningful contributions to the seeker’s or the platform’s adoption strategies between two alternative contest formats in crowdsourcing practice.

The purpose of this paper is to explore the underlying mechanisms through which interactivity and fairness perception impart influence on solvers’ continuance intention in crowdsourcing contest settings.

On basis of self-determination theory and social exchange theory, this study focuses on the mediating roles of motivation and platform trust to explain the underlying influence processes of interactivity and fairness perception on continuance intention. A sample of 306 solvers was obtained from an online crowdsourcing platform through two separated surveys. The hypotheses were tested using the partial least squares method and bias-corrected bootstrapping method.

The empirical results indicate that motivation and platform trust together fully mediate the effect of interactivity on continuance intention, and the effect of fairness perception on continuance intention is also fully mediated by motivation and platform trust. While motivation is found to have a stronger mediating effect than platform trust does.

This study contributes to the crowdsourcing research by figuring out the pathway through which interactivity and fairness perception influence solvers’ continuance intention.

Crowdsourcing delivers creative ideas for the issuing firm, but participants’ engagement in the creative process also creates additional benefits to firms and participating customers. The purpose of this study is to investigate if these spill-over values endure over time. With data from two time point, i.e. at submission and after announcement of the contest winners, we examine the relationship between the degree of a participant’s creative process engagement (CPE) and value creation from a crowdsourcing contest, and how these perceptions of value change over time.

Data were collected from 154 participants in a crowdsourcing contest at two time points with an online survey: at submission, and after receiving feedback (in term of rankings, rewards, and comments) from the community. Partial Least Square path modelling was used to estimate both main and moderating effects.

CPE increases the perceived value of customers (social and epistemic value) and firms alike (knowledge-sharing intention and customer loyalty), though all but epistemic values decrease over time. Disconfirmation of expectations and need for recognition moderate these effects.

This paper is the first longitudinal study that helps understanding the effect of CPE on value creation from crowdsourcing across time. It also uses the theoretical lens of the honeymoon hangover effect to explain how perceived value changes. The resulting insights into the role of customer engagement in crowdsourcing contests and subsequent value creation will be beneficial to the growing research stream on consumer value co-creation and user innovation.

Existing studies on the relationship between task description and task performance are insufficient, with many studies considering description length rather than content to measure quality or only evaluating a single aspect of task performance. To address this gap, this study analyzes the linguistic styles of task descriptions from 2,545 tasks on the Taskcn.com crowdsourcing platform.

An empirical analysis was completed for task description language styles and task performance. The paper used text mining tool Simplified Chinese Linguistic Inquiry and Word Count to extract eight linguistic styles, namely readability, self-distancing, cognitive complexity, causality, tentative language, humanizing personal details, normative information and language intensity. And it tests the relationship between the eight language styles and task performance.

The study found that more cognitive complexity markers, tentative language, humanized details and normative information increase the quantity of submissions for a task. In addition, more humanized details and normative information in a task description improves the quality of task. Conversely, the inclusion of more causal relationships in a task description reduces the quantity of submissions. Poorer readability of the task description, less self-estrangement and higher language intensity reduces the quality of the task.

This study first reveals the importance of the linguistic styles used in task descriptions and provides a reference for how to attract more task solvers and achieve higher quality task performance by improving task descriptions. The research also enriches existing knowledge on the impact of linguistic styles and the applications of text mining.

The purpose of this paper is to develop an optimal charging strategy for a third-party crowdsourcing platform.

Based on the auction theory, the Stackelberg game theory and the systems theory, this paper presents a new model from the perspective of risk sharing between solution seekers and the crowdsourcing platform, given the utility maximization of the seekers, the crowdsourcing platform and the solvers.

Based on the results, this study shows that the menu of fees, which includes different combinations of a fixed fee and a floating fee schedule, should be designed to attract both solution seekers and solvers. In addition, the related prize setting and the expected payoff for each party are presented.

This study is beneficial for crowdsourcing platform operators, as it provides a new way to design charging strategies and can help in understanding key influential factors.

To the best of the authors’ knowledge, this study is one of the first to simulate the interactions among the three stakeholders, thereby providing a novel model that includes a fixed fee and a floating commission.