Mobile Crowdsourcing (MCS), a human-centric promising paradigm for performing location-based tasks, has drawn rising attention from both academia and industry. In MCS applications, the outsourced tasks are allocated by a management platform to a group of recruited workers. However, during real-world task implementation, various types of unpredictable disruptions are usually inevitable, which might result in task execution failure, and subsequently hinder the development of MCS applications. Facing the task execution failure issue, centralized task reassignment approaches thus become ineffective and inefficient in practice. Against this background, by exploring the underlying social relationship between workers, we consider a distributed MCS task offloading scheme, i.e., the workers autonomously offload the unexecuted MCS tasks to their social acquaintances. However, to efficiently design such offloading mechanisms, we are facing several challenges, including investigating the relevant influential factors in task offloading, designing offloading patterns and incentive mechanism to accommodate it. To address these challenges, in this paper, we conduct an on-campus empirical study on MCS task offloading on social collaboration networks. Firstly, we conduct a survey covering over 1000 workers to capture the preliminary understanding of it. Based on the survey results, we then conduct a “field experiment” over a deployment period of 8 weeks, to comprehensively examine the intrinsic characteristics and behavioral patterns in task offloading, including the effectiveness of task offloading scheme, the offloadee selection, the impact of punitive measures, the adopted task offloading patterns, and reward-sharing incentive mechanism. By analyzing the collected operation logs of the workers, we summarize several important findings on the design of task offloading scheme in MCS applications, which we believe, can serve as useful guidelines for future research work on MCS task offloading.