Improving the effectiveness of social media-based crowdsourcing innovations: roles of assurance mechanism and innovator's behaviour

Abstract

PurposeWith the development of social media and Internet technology, many firms have started to use various crowdsourcing innovation platforms to operate their open innovation business modes. The purpose of this study is to explore how such platforms' assurance mechanisms enhance the effectiveness of crowdsourcing innovations and how to apply assurance mechanisms to handle different innovation tasks, thereby motivating more seekers to use crowdsourcing innovations.Design/methodology/approachThe authors use a Python-based technology to collect the research data comprising 2,302 solvers and 8,390 trade records from zbj.com and apply statistical methods to test the postulated hypotheses.FindingsThe effectiveness of assurance mechanism is confirmed by its positive relationship with solver's behaviour, thereby improving seeker's retention behaviour. However, task complexity, task novelty and task professionalization have different moderating effects on the relationships among assurance mechanism, solver's (innovator's) behaviour and seeker's behaviour.Research limitations/implicationsThis study enriches the literature on crowdsourcing innovations and extends the application of uncertainty reduction theory to innovation research. It also makes the theoretical contribution that the assurance mechanism adopted by the platform has different impacts on user's behaviour depending on the task characteristics.Practical implicationsThe findings provide guidance to the platform operator on how to design the assurance mechanism to match the innovation task and innovator's behaviour to reduce seeker's uncertainty, thereby facilitating the seeker's decision-making.Originality/valueA particular value of this study lies in exploring the impact of the platform assurance mechanism of social media-based crowdsourcing innovations on innovator's behaviour, which may further improve seeker's behaviour, based on uncertainty reduction theory.