Solo Dance or Pas de Deux? The Sector Stereotype Matters in Coproduction

AbstractThis study examines the influence of sector stereotypes on citizens' willingness to engage in coproduction. We explore the relationships between different sector tags and citizens' coproduction willingness in the domains of recycling and public safety, using a 2 × 3 experimental design. The findings indicate that citizens show significantly higher coproduction willingness under the government tag compared to the nonprofit tag in recycling but not public safety. In both recycling and public safety, the government–nonprofit nexus significantly enhances individuals' willingness to engage in coproduction, surpassing the effects of the government or nonprofit tag alone. Furthermore, volunteer experience positively moderates the relationship between the government tag and coproduction willingness while negatively moderating the government–nonprofit nexus and coproduction willingness. Individuals with varying degrees of volunteer experience may perceive coproduction initiatives differently depending on the initiating entity, resulting in differences in willingness to coproduce, possibly due to factors such as role clarity and autonomy. These findings underscore the substantial influence of sector identity on coproduction and highlight the importance of service sector compatibility for effectively mobilizing citizens in coproduction efforts.Keywords: coproductiongovernment–nonprofit nexussector stereotypesurvey experiment Notes1 See Hangzhou 760,000 security patrol volunteers escort G20 (Wang, Citation2016); Hangzhou was commended for the "460,000 Volunteers in the City" activity of domestic waste sorting by central government in 2019 (Bureau of Municipal Affairs in Hangzhou, Citation2020).2 The socioeconomic heterogeneity within these communities resulted in varying intra-community conditions, with diverse social groups such as migrant workers, urban residents, and relocated rural residents affected by urbanization policies. Cluster sampling was utilized in communities with diverse social groups, whereby the population was divided into clusters based on social groupings, and sampling quotas were allocated to each cluster proportional to their population size. A random sample was then taken from each cluster. In communities dominated by a single social group, a systematic sampling approach was employed, selecting samples at fixed intervals from the community roster and adjusted for population size. The selection of the 20 communities ensured their representativeness in terms of population size, socioeconomic conditions, and geographic location within Hangzhou.3 See the detailed 2019 data from Hangzhou's government website (Citation2020) and Municipal Bureau of Statistics in Hangzhou (Citation2020)Additional informationFundingThis work was funded by the National Natural Science Foundation of China (72204088).Notes on contributorsFan YangFan Yang is an associate professor in the School of Government, East China University of Political Science and Law. His research focuses on street-level bureaucracy and local policy implementation.Yong LiYong Li is a lecturer in the School of Marxism, Shanghai Maritime University. Her research encompasses political trust and NPO management.