Social Media, Influencers, and Adoption of an Eco-Friendly Product: Field Experiment Evidence from Rural China

被引:88
|
作者
Zhang, Wanqing [1 ]
Chintagunta, Pradeep K. [2 ]
Kalwani, Manohar U. [3 ]
机构
[1] City Univ London, Cass Business Sch, London, England
[2] Univ Chicago, Mkt, Booth Sch Business, Chicago, IL 60637 USA
[3] Purdue Univ, Management, Krannert Sch Management, W Lafayette, IN 47907 USA
关键词
emerging markets; field experiment; innovation adoption; mobile marketing; social media; eco-friendly; sustainability; WORD-OF-MOUTH; DIFFUSION; LOGIT; EXTENSION; BEHAVIOR; IMPACT;
D O I
10.1177/0022242920985784
中图分类号
F [经济];
学科分类号
02 ;
摘要
Can low-cost marketing tools that are used to enhance business performance also contribute to creating a better world? The authors investigate the role of online social media tools in alleviating customer (farmer) uncertainty and promoting the adoption of a new eco-friendly pesticide in rural China via a randomized controlled field experiment. The key finding is that even for a new product such as a pesticide, a low-cost social media support platform can effectively promote adoption. A combination of information from peers and from the firm on the platform facilitates learning about product features and alleviates uncertainty associated with product quality and appropriate product usage. Nevertheless, at the trial stage of the funnel, the platform underperforms the firm's customized one-on-one support because available information does not resolve uncertainty in supplier credibility and product authenticity. Having an influencer on the platform, albeit not an expert on this product, vouching for its credibility helps resolve this funnel-holdup problem. From a theoretical perspective, this paper provides suggestive evidence for referent influence and credibility signaling on social media platforms and consequences for new product trial. The authors also provide direct empirical evidence on how information facilitates learning, a phenomenon typically assumed to be present in studies estimating learning models.
引用
收藏
页码:10 / 27
页数:18
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