Submitting tentative solutions for platform feedback in crowdsourcing contests: breaking network closure with boundary spanning for team performance

Khasraghi, Hanieh Javadi; Wang, Xuan; Sun, Jun; Khasraghi, Bahar Javadi

doi:10.1108/itp-10-2021-0831

Cited by 2 publications

(1 citation statement)

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“…The previous research has mainly focused on four categories of factors that may influence individuals' behaviors in crowdsourcing platforms or contests: (1) task-specific factors (reward, task type, task complexity and contest duration for a task, etc.) (Archak, 2010; Boudreau et al ., 2011; Huang et al ., 2011; Liu et al ., 2007; Terwiesch and Xu, 2008; Deng and Joshi, 2016; Geri et al ., 2017); (2) individual-specific factors (extrinsic motivations, intrinsic motivations, individuals' strategy and individuals' experience) (Archak, 2010; Bayus, 2013; Huang et al ., 2011; Yang et al ., 2010; Deng and Joshi, 2016; Geri et al ., 2017; Ye and Kankanhalli, 2017); (3) environment-specific factors (competitors' rating, number of competitors, number of super-star competitors, number of non-super-star competitors, communication, network closure and boundary spanning) (Boudreau et al ., 2012; Bullinger et al ., 2010; Hutter et al ., 2011; Javadi Khasraghi et al ., 2022) and (4) organization-specific factors (brand-strength and marketplace maturity) (Yang et al ., 2009). Table 1 provides a summary of the factors affecting individuals' behaviors on crowdsourcing platforms.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Contribution to team and community in crowdsourcing contests: a qualitative investigation

Khasraghi

Vaghefi

Hirschheim

2023

ITP

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PurposeThe 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.Design/methodology/approachThe 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.FindingsThe 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.Research limitations/implicationsThe 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).Practical implicationsThe 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.Social implicationsThe 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.Originality/valueThe 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.

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