logo
Loading...
Factors influencing continuance intention of participants in crowdsourcing

Business

Factors influencing continuance intention of participants in crowdsourcing

H. Jo and Y. Bang

Explore the intriguing dynamics of user motivation in crowdsourcing contests! This study, conducted by Hyeon Jo and Youngsok Bang, reveals how factors like utilitarian motivation and career promotion can drive participants to stay engaged. Discover what keeps 291 Korean participants coming back for more!... show more
Introduction

The paper examines what drives participants in crowdsourcing and idea competitions to continue engaging over time. While initial participation may stem from curiosity or extrinsic rewards, the determinants of sustained participation are less understood. The study posits that aligning personal goals with platform outcomes (goal-congruent outcomes, GCO) and participants’ search intention within portals are pivotal. It proposes that utilitarian motivation, originality, career promotion, and rewards may shape GCO and search intention, which in turn influence continuance intention. The research question is: What factors influence GCO, search intention, and continuance intention among users of contest collection portals?

Literature Review

Theoretical background synthesizes work on crowdsourcing’s effectiveness for innovation and problem solving, and motivation theory distinguishing intrinsic (e.g., enjoyment, learning, originality) and extrinsic (e.g., monetary rewards, recognition, career advancement) drivers. Prior research has addressed isolated determinants (peer recognition, intrinsic vs. extrinsic motivation, organizational adoption) but lacks an integrated framework linking motivations to mediators like goal congruence and search intention. Studies highlight roles of feedback, collaboration, competition intensity, participant attributes, and contest design in participation and performance. The authors identify a gap regarding the combined roles of GCO and search intention in explaining continuance intention, motivating hypotheses: utilitarian motivation, originality, career promotion, and rewards positively affect GCO and search intention, which then drive continuance intention.

Methodology

Design: Cross-sectional survey analyzed with Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS. Measures: All items measured on 7-point Likert scales. Constructs and sources: utilitarian motivation (Salehan et al., 2017), originality (Dean et al., 2006), career promotion (Wook & Jongho, 2018), rewards (Walter & Back, 2011), GCO (Seo & Ray, 2019), search intention and continuance intention (Ajzen, 1991). Questionnaire developed in English, translated to Korean and back-translated; expert review and pilot testing conducted. Sampling and data collection: Online intercept survey on South Korean contest portals (all-con.co.kr, thinkyou.co.kr, allforyoung.com) from March 18–27, 2022. Visitors saw a pop-up inviting participation and provided informed consent. Attention checks, reverse coding, and mandatory responses used. Incentivized via a lottery. After removing unauthentic responses, N=291 retained. Sample power check via a-priori SEM calculator indicated minimum 247; achieved sample exceeded this. Sample profile (N=291): 32.6% male, 67.4% female; age: majority 20–29 (56.7%); largest occupation group university students (45.7%). Assessment of measurement model: Reliability supported (Cronbach’s alpha and Composite Reliability > 0.70). Convergent validity supported (item loadings > 0.70; AVE > 0.50). Discriminant validity supported via Fornell-Larcker criterion and HTMT. Common method bias: Harman’s single-factor test showed 34.174% variance (<50% threshold). VIF values <5 indicate no problematic multicollinearity. Endogeneity: Gaussian copula approach in PLS-SEM indicated no significant copulas (p>0.05) and variables treated as exogenous, mitigating endogeneity concerns. Model estimation: Two-stage PLS approach. R² values: continuance intention 0.63, GCO 0.21, search intention 0.10. Control variables: gender and age.

Key Findings

Hypothesis tests (standardized path coefficients, p-values):

  • H1a Utilitarian motivation → GCO: β=0.308, t=4.647, p<0.001 (Supported)
  • H1b Utilitarian motivation → Search intention: β=0.270, t=3.280, p=0.001 (Supported)
  • H1c Utilitarian motivation → Continuance intention: β=0.000, t=0.086, p=0.932 (Not supported)
  • H2a Originality → GCO: β=0.029, t=0.471, p=0.638 (Not supported)
  • H2b Originality → Search intention: β=0.070, t=0.725, p=0.469 (Not supported)
  • H2c Originality → Continuance intention: β=0.048, t=1.585, p=0.113 (Not supported)
  • H3a Career promotion → GCO: β=0.158, t=2.243, p=0.025 (Supported)
  • H3b Career promotion → Search intention: β=−0.043, t=−0.650, p=0.510 (Not supported)
  • H3c Career promotion → Continuance intention: β=−0.036, t=−0.757, p=0.449 (Not supported)
  • H4a Rewards → GCO: β=0.135, t=2.089, p=0.037 (Supported)
  • H4b Rewards → Search intention: β=0.127, t=1.985, p=0.047 (Supported)
  • H4c Rewards → Continuance intention: β=0.060, t=1.307, p=0.191 (Not supported)
  • H5 GCO → Continuance intention: β=0.258, t=4.749, p<0.001 (Supported)
  • H6 Search intention → Continuance intention: β=0.622, t=12.498, p<0.001 (Supported) Control variables: Gender (β=0.048, p=0.570) and age (β=0.003, p=0.938) not significant. Model explanatory power: R²=0.63 for continuance intention; R²=0.21 for GCO; R²=0.10 for search intention. Overall: Utilitarian motivation and rewards positively influence GCO and search intention; career promotion positively influences GCO only; originality is non-significant. Continuance intention is strongly driven by search intention and, to a lesser extent, by GCO.
Discussion

Findings address the research question by showing that continued participation in crowdsourcing is less about direct utility or originality per se and more about whether participants perceive outcomes aligned with their goals (GCO) and maintain an intention to search and explore opportunities on portals. Utilitarian motivation is a key antecedent to both GCO and search intention, indicating functional value prompts users to align goals and actively explore contests. Rewards also encourage both alignment and exploration, but do not directly sustain continuance without the mediating roles of GCO and search. Career promotion matters insofar as it enhances GCO, reinforcing the importance of perceived professional progress. Originality did not significantly affect any pathway, suggesting novelty alone does not translate into sustained engagement. The strong effect of search intention on continuance intention underscores exploration as a central mechanism for ongoing engagement. These results suggest platform strategies should prioritize facilitating goal alignment and exploratory behaviors to support long-term participation.

Conclusion

The study contributes an integrated framework linking motivational factors to continuance intention via GCO and search intention among users of crowdsourcing contest portals. It demonstrates that utilitarian motivation and rewards enhance both goal congruence and exploration, career promotion enhances goal congruence, and that continuance intention is primarily driven by search intention and supported by GCO. Theoretical contributions include highlighting GCO as a key mediator and reframing search intention as a strong predictor of continuance. Practical implications recommend aligning incentives with diverse participant goals, recognizing rewards as both monetary and symbolic, and designing platforms to foster exploration (e.g., tailored content, feedback mechanisms, career-enhancing opportunities). Future research should examine these dynamics across cultures and sectors, leverage longitudinal designs, and explore psychological dimensions of rewards and the roles of emerging technologies.

Limitations

Key limitations include: cross-sectional design limiting causal inference; sample concentrated on South Korean contest portals and skewed toward younger and student populations, limiting generalizability; potential sample selection bias from voluntary participation; reliance on quantitative self-report measures without qualitative insights; and focus on portal users rather than cross-platform behaviors. Future work should employ longitudinal and mixed-method designs, assess cultural and industrial differences, consider emerging technologies (e.g., AI, blockchain), and broaden to multiple platforms and populations.

Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny