Engaging customers is a critical requirement for sharing economy platforms (SEPs) to sustain and grow their user base. Although the interactions between users who consume the service (customers) and those who provide it (peer service providers) are the primary source of SEPs’ economic value, little is known about the role of interactivity in driving customer engagement. This research links these two important concepts by theorizing and empirically testing the influence of different dimensions of interactivity (two-way communication, participation, joint problem-solving) on customer engagement (cognitive engagement, emotional engagement, behavioral engagement) in SEPs.

Ocean economy plays a crucial role in the strengthening maritime safety industry and in the welfare of human beings. Electric Submersible Pumps (ESP) have been widely used in floating platforms on the sea to provide oil for machines. However, the ESP fault may lead to ocean environment pollution, on the other hand, a timely fault diagnosis of ESP can improve the ocean economy. In order to meet the strict regulations of the ocean economy and environmental protection, the fault diagnosis of ESP system has become more and more popular in many countries. The vibration mechanical models of typical faults have been able to successfully diagnose the faults of ESP. And different types of sensors are used to monitor the vibration signal for the signal analysis and fault diagnosis in the ESP system. Meanwhile, physical sensors would increase the fault diagnosis challenge. Nowadays, the method of neural network for the fault diagnosis of ESP has been applied widely, which can diagnose the fault of an electric pump accurately based on the large database. To reduce the number of sensors and to avoid the large database, in this paper, algorithms are designed based on feature extraction to diagnose the fault of the ESP system. Simulation results show that the algorithms can achieve the prospective objectives superbly.