Purpose: Although health question-and-answer (Q&A) communities have become popular in recent years, only a few communities have successfully retained and motivated their members to share knowledge. The purpose of this paper is to focus on the ways by which social capital and motivation influence knowledge sharing intention from the perspectives of health professionals and normal users in health Q&A communities.Design/methodology/approach: The developed theoretical model integrates individual motivation and social capital theories. On the basis of a sample comprising 363 members from health Q&A communities in China, the authors tested the hypotheses by using structural equation modeling.Findings: This study empirically finds that social capital positively affects intrinsic and extrinsic motivations, which then positively influence the intention of health professionals and normal users to share knowledge. Motivations of members fully mediate the effects of social capital on knowledge sharing intention. Specifically, intrinsic motivation influences knowledge sharing intention more for health professionals than for normal users, whereas extrinsic motivation influences knowledge sharing intention more for normal users than for health professionals.Originality/value: This study explores the factors that affect the intentions of sharing knowledge in health Q&A communities by integrating social capital and motivation theories. Individual motivations can then bridge social capital and knowledge sharing intention. The effects of the intrinsic and extrinsic motivations of two user types were further examined and compared. These findings can extend the understanding of the underlying drivers of intention to share knowledge in the context of e-health.

We study robotic mobile fulfilment systems for online retailers, where products are stored in movable shelves and robots transport shelves. While previous studies assume random assignment rule of workstations to robots, we propose an assignment rule based on handling speeds of workstations and design a neighbourhood search algorithm to find a near optimal assignment rule. We build semi-open queueing networks and use a two-phase approximate approach for performance estimation. We first replace workstation service processes by a composite service node and then solve the model by the matrix-geometric method. Simulations are used to validate the analytical models. Numerical experiments are conducted to compare random, handling-speeds-based, near optimal and optimal assignment rules, in terms of retrieval throughput time. The results show that the random assignment rule is not a good choice, the handling-speeds-based assignment rule significantly outperforms the random assignment rule when the workers have large handling time difference, and the neighbourhood search approach can provide an assignment rule that is very close to the optimal one, using a much shorter time. Moreover, we design the shelf blocks under the examined assignment rules, and find that the optimal width of shelf block decreases with the width to length ratio.