Developer question and answering communities rely on experts to provide helpful answers. However, these communities face a shortage of experts. To cultivate more experts, the community needs to quantify and analyze the rules of the influence of extrinsic motivations on the ongoing contributions of those developers who can become experts in the future (potential experts). Currently, there is a lack of potential expert‐centred research on community incentives. To address this gap, we propose a motivational impact model with self‐determination theory‐based hypotheses to explore the impact of five extrinsic motivations (badge, status, learning, reputation, and reciprocity) for potential experts. We develop a status‐based timeline partitioning method to count information on the sustained contributions of potential experts from Stack Overflow data and propose a multifactor assessment model to examine the motivational impact model to determine the relationship between potential experts’ extrinsic motivations and sustained contributions. Our results show that (i) badge and reciprocity promote the continuous contributions of potential experts while reputation and status reduce their contributions; (ii) status significantly affects the impact of reciprocity on potential experts’ contributions; (iii) the difference in the influence of extrinsic motivations on potential experts and active developers lies in the influence of reputation, learning, and status and its moderating effect. Based on these findings, we recommend that community managers identify potential experts early and optimize reputation and status incentives to incubate more experts.

Motivations are vital for developers to choose and remain in developer question and answering (Q&A) communities to maintain the prosperity of these communities. Hence, they have been investigated by many researchers in the past decade. However, a systematic research overview has yet to be performed in this domain, leaving a fragmented picture of developers' motivations for participating in these communities. To fill this gap, this work (1) groups developer motivations from multiple data sources by a deep text clustering method, (2) statistically identifies determining factors of developers, and (3) analyzes the regulation styles of the motivation determinants based on the self-determination theory. Taking Stack Overflow as the research context, this study collected 134 motivations from 41 related research. With the motivations collected, this study took them into seventeen clusters. The centres of eight were considered the determining factors: reputation, badge, getting information, learning, reciprocity, helping others, obtaining privileges, and fun. These motivational determinants fall under five regulation styles, from intrinsic to extrinsic. The findings presented a systematic picture of developers' motivations for participating in Stack Overflow and can help researcher and practitioners optimize strategies to stimulate developers to build sustainable online communities.