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Social networks offer various services such as recommendations of social events, or delivery of targeted advertising material to certain users. In this work, we focus on a specific type of services modeled as constrained graph partitioning (CGP). CGP assigns users of a social network to a set of classes with bounded capacities so that the similarit...
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Purpose
The incentive cost of enterprises increases significantly with the rapid growth of the social commerce (SC) market. In this context, enterprises need to develop the optimal strategy to improve incentive effectiveness and reduce cost. Different types of consumers’ responses to incentives bring different values to enterprises. Hence, this paper proposes the social commerce value network (SCVN) to help enterprises study the contributions of different types of consumers to the network.
Design/methodology/approach
Based on the graphical evaluation and review technique (GERT), the authors construct the social commerce value GERT (i.e. SCV-GERT) network and design three progressive experiments for estimating the value contributions of “network stage”, “consumer type”, and “resource type” to the SCVN under the same incentives. The authors initialize the SCV-GERT model with consumer data in SC and distinguish the most valuable consumers by adjusting the incentive parameters.
Findings
The results show that the SCV-GERT model can well describe the value flow of SCVN. The incentive on forwarding consumers brings the greatest value gain to the SCVN, and social trust contributes the most to forwarding consumers.
Practical implications
Under the guidance of the results, platforms and enterprises in SC can select the optimal type of consumers who bring the maximum network value so as to improve the effectiveness of incentive strategy and reduce marketing costs. A four-level incentive system should be established according to the ranking of the corresponding value gains: forwarding consumers > agent consumers > commenting consumers > potential consumers. Enterprises also need to find ways to improve the social resource investments of consumers participating in SC.
Originality/value
This paper investigates the incentive problem in SC grounded in the SCVN and uses the GERT method to construct the SCV-GERT model, which is the first attempt to introduce GERT into the SC context. This study also makes up for the lack of comparative research on different types of consumers in SC and can provide support for enterprises’ customer relationship management and marketing decisions.
Due to the increasing volume of data to be analyzed and the need for global collaborations, many scientific applications have been deployed in a geo-distributed manner. Scientific workflows provide a good model for running and managing geo-distributed scientific data analytics. However, due to the multi-level data privacy requirements in geo-distributed data centers (DCs), as well as the costly and heterogeneous inter-DC network performance, executing scientific workflows efficiently in such a geo-distributed environment is not easy. In this paper, we propose a privacy-preserving workflow scheduling algorithm named PPPS, which aims at minimizing the inter-DC data transfer time for workflows while satisfying data privacy requirements. We compare PPPS with five state-of-the-art workflow scheduling algorithms using Windows Azure cloud performance traces and real scientific workflows. Experimental results show that PPPS can greatly reduce the workflow execution time compared to the other algorithms by up to 93% while satisfying complicated data privacy constraints.
