RETRACTED ARTICLE: Factors influencing continuance intention of participants in crowdsourcing

Jo, Hyeon; Bang, Youngsok

doi:10.1057/s41599-023-02335-0

Cited by 2 publications

(2 citation statements)

References 104 publications

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“…While a significant number R&D departments use crowdsourcing to gather awareness and impressions about their products or services, it is also important to consider the perspective of users who want to participate in the co-creation of a product. It is important to understand and cater to users' diverse motivations in order to keep them interested and engaged (Jo & Bang, 2023).…”

Section: Discussionmentioning

confidence: 99%

The impact of crowdsourcing and user-driven innovation on R&D departments’ innovation activity: Application of multivariate correspondence analysis

Szopik-Depczyńska,

Dembińska,

Barczak

et al. 2024

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Research background: In enterprise practices, innovation management is taking on more and more open forms. New, creative sources of inspiration are being sought. More and more companies are opening up to cooperation with external people or entities, thus entering a path of open innovation and crowdsourcing. This development is facilitated by increasingly large numbers of online and virtual communities. The idea is to use the potential of the crowd — collective intelligence and creativity. As the strategy is still developing, knowledge in this area is limited. There is a paucity of research on the impact of crowdsourcing on R&D departments and their innovative activities. Purpose of the article: The study aims to determine whether the use of customer support in the form of crowdsourcing affects the innovative activities of R&D departments that implement user-driven innovation (UDI). Methods: An original questionnaire was used for the study. Fifty-seven R&D departments in Poland participated in the research. A correspondence analysis, performed on the basis of the Burt matrix, was applied for the analysis. The authors also used Cramer's V correlation coefficients. Examination of the acquired correlation coefficients reveals the existence of four distinct categories of enterprises regarding the usage of crowdsourcing and implementation of product innovations, completed R&D projects, employment in R&D departments, and percentage of revenue allocating in R&D works. Findings & value added: Research has shown that R&D departments positively evaluate the effects of using UDI in market research analyses of customer purchasing behavior. It helps to create or improve products or services offered on the market, especially in the field of customization, using the information from national research and development units at the same time. Their purpose is mainly to increase the product range. The findings can help academics and practitioners improve the body of knowledge about the determinants of innovation activity, especially concerning relationships with customers and user-driven innovation practices.

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Section: Discussionmentioning

confidence: 99%

The impact of crowdsourcing and user-driven innovation on R&D departments’ innovation activity: Application of multivariate correspondence analysis

Szopik-Depczyńska,

Dembińska,

Barczak

et al. 2024

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“…Note that the chosen SOTA method for comparison represents the most recent machine/deep learning approach for CCSD success prediction. Although other recent studies [43,54] are available, they provide empirical evaluations of CCSD success prediction. Consequently, these studies cannot be included in comparisons with the proposed approach.…”

Section: Research Questionsmentioning

confidence: 99%

Success Prediction of Crowdsourced Projects for Competitive Crowdsourced Software Development

Rashid,

Anwar,

Jaffar

et al. 2024

Applied Sciences

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Competitive Crowdsourcing Software Development (CCSD) is popular among academics and industries because of its cost-effectiveness, reliability, and quality. However, CCSD is in its early stages and does not resolve major issues, including having a low solution submission rate and high project failure risk. Software development wastes stakeholders’ time and effort as they cannot find a suitable solution in a highly dynamic and competitive marketplace. It is, therefore, crucial to automatically predict the success of an upcoming software project before crowdsourcing it. This will save stakeholders’ and co-pilots’ time and effort. To this end, this paper proposes a well-known deep learning model called Bidirectional Encoder Representations from Transformers (BERT) for the success prediction of Crowdsourced Software Projects (CSPs). The proposed model is trained and tested using the history data of CSPs collected from TopCoder using its REST API. The outcomes of hold-out validation indicate a notable enhancement in the proposed approach compared to existing methods, with increases of 13.46%, 8.83%, and 11.13% in precision, recall, and F1 score, respectively.

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RETRACTED ARTICLE: Factors influencing continuance intention of participants in crowdsourcing

Cited by 2 publications

References 104 publications

The impact of crowdsourcing and user-driven innovation on R&D departments’ innovation activity: Application of multivariate correspondence analysis

The impact of crowdsourcing and user-driven innovation on R&D departments’ innovation activity: Application of multivariate correspondence analysis

Success Prediction of Crowdsourced Projects for Competitive Crowdsourced Software Development

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