Contribution to team and community in crowdsourcing contests

A qualitative investigation

Datos Bibliográficos

ID21596011
AutoresHanieh Javadi Khasraghi (0000-0001-8434-9370, University of Delaware), Isaac Vaghefi (0000-0002-8143-0832, Baruch College), Rudy Hirschheim (0000-0002-1929-6097, Louisiana State University)
Año2024
Volumen37
Número1
Páginas223-250
Fecha de publicación2024-01-09
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaInformation Technology and People (JOURNAL)
Identificadores de la revistaISSN: 0959-3845 • E-ISSN: 1758-5813
EditorialEmerald (PUBLISHER)
DOI10.1108/itp-01-2021-0069
OpenAlexW4313572575
IdiomaEN
Citas recibidas3
Referencias citadas46

Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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). Practical implications 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. Social implications 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. Originality/value 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

Business · Crowdsourcing · Data science · Knowledge management · Qualitative research · Social science · Sociology · Thematic analysis · Typology · World Wide Web · Computer Science · Digital Marketing and Social Media · Knowledge Management and Sharing · Open Source Software Innovations · Psychology · Marketing

Herramientas

Abrir DOI