Coevolution of Trust and Helping Behavior: A Social Network Examination of Dyadic and Third‐Party Influences
基于中国医院的三波社会网络数据，使用随机行动者模型，研究了信任与帮助行为在二元和三元关系中的动态共同演化，发现帮助促进信任、信任可通过第三方传递，且员工更可能信任间接帮助者或帮助共同信任的同事。
ABSTRACT Trust and helping both reflect positive relationships between organizational members and are vital to organizational success. Although prior research has demonstrated their connection with each other, most studies have examined trust and helping behavior in a static manner. Relatedly, although recent research has started to utilize network methods to consider the role of the surrounding social context in shaping trust, these inquiries remain limited by a static, aggregate view, looking at network structures such as one's network size or position in the network. The current study aims to advance our understanding by examining how trust and helping behaviors coevolve dynamically within social networks, incorporating both dyadic and triadic (third‐party) influences. Integrating social information processing theory with social exchange theory, the current study explores how individuals develop trust and engage in helping behaviors in a triad through interacting with each other and observing the informational and social elements in the triad. Based on three‐wave social network data from a Chinese hospital, we used stochastic actor–based modeling to examine the dynamic coevolution of trust and helping ties over time. The results demonstrated that trust and helping networks evolve reciprocally, influenced by the types of relationships and agents involved. Specifically, interpersonal helping fosters trust within dyads, and trust can be transferred through third‐party connections. Employees are more likely to trust a coworker when they receive indirect help from the coworker or when the coworker engages in helping behaviors with others. Employees also tend to help a coworker when both are trusted by a third party. Overall, these results highlight the importance of third‐party influences in shaping trust and helping behaviors, offering new insights for both theory and practice.