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.

中文翻译：

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使用图形评估和审查技术区分社交商务中最有价值的消费者——从激励的角度来看

目的

随着社交商务（SC）市场的快速增长，企业的激励成本显着增加。在此背景下，企业需要制定最优策略，提高激励效果，降低成本。不同类型的消费者对激励的反应给企业带来不同的价值。因此，本文提出社交商务价值网络（SCVN）来帮助企业研究不同类型消费者对网络的贡献。

设计/方法/方法

基于图形评价与评论技术（GERT），作者构建了社交商务价值GERT（即SCV-GERT）网络，并设计了三个渐进式实验来估计“网络阶段”、“消费者类型”和“消费者类型”的价值贡献。资源类型”到 SCVN 在相同的激励下。作者用 SC 中的消费者数据初始化 SCV-GERT 模型，并通过调整激励参数来区分最有价值的消费者。

发现

结果表明，SCV-GERT模型能够很好地描述SCVN的价值流向。转发消费者的激励为SCVN带来最大的价值收益，社会信任对转发消费者的贡献最大。

实际影响

在结果的指导下，SC中的平台和企业可以选择能够带来最大网络价值的最优消费者类型，从而提高激励策略的有效性，降低营销成本。建立四级激励体系，按照相应的价值收益排序：转发消费者>代理消费者>评论消费者>潜在消费者。企业还需要想办法提高参与SC的消费者的社会资源投资。

原创性/价值

本文以SCVN为基础研究了SC中的激励问题，并采用GERT方法构建了SCV-GERT模型，这是将GERT引入SC上下文的首次尝试。本研究也弥补了SC不同类型消费者比较研究的不足，可为企业客户关系管理和营销决策提供支持。