叫座却不叫好:明星效应对网络口碑的影响
Journal of management science（2020）
Abstract
在互联网时代,明星和口碑均是现代营销沟通理论中的关键元素.在电商网站和社交媒体等互联网平台,通过明星效应引发网络口碑是企业在进行互动营销活动中的常用方法.然而,已有关于明星效应的研究主要集中于探讨明星效应对产品销量和企业价值的影响及作用机理,较少涉及明星效应在网络互动营销中发挥的作用.考察明星效应对网络口碑的影响,分析明星效应与网络口碑的数量、效价和差异3个维度之间的关系.2018年1月运用网络爬虫程序从大众点评网站上采集京沪两地超过6 000个餐饮商户的大规模样本,通过倾向得分匹配方法对明星商户样本和非明星商户样本进行有效匹配,以解决潜在的内生性问题;建立计量回归模型,对明星效应与网络口碑的数量、效价和差异之间的关系进行系统的实证分析.研究结果表明,明星效应对网络口碑的数量、效价和差异3个维度均存在显著的影响.①在口碑数量方面,明星效应显著促进消费者对餐饮商户的评论数量;②在口碑效价方面,明星效应对商户的总体评分的影响不显著,但对商户的具体属性评分有显著的负向影响;③在口碑差异方面,明星效应显著增加消费者对商户的评分差异.明星效应对网络口碑的影响存在“叫座却不叫好”的现象,研究结果有助于人们理解明星效应在网络互动营销活动中发挥的重要作用,也为企业的网络互动营销管理实践提供指导建议,并提示营销管理者在运用明星效应引发网络口碑时,需要重点权衡口碑数量与口碑效价.企业也应充分考虑产品的互动营销目标,从而更好地发挥明星效应的作用.
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Pretraining has recently greatly promoted the development of natural language processing (NLP)We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performanceWe propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generationThe model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in ChineseExperimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performanceUpload PDF to Generate Summary
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