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市场营销系列学术讲座(第七十三讲)--When Service Agents Defect with Customers From Online Service Platforms

编辑者:沈阿平 | 发布时间:2020-09-11

演讲题目:When Service Agents Defect with Customers From Online Service Platforms(平台剥削:当服务提供者与顾客共同绕开线上服务平台)

演讲嘉宾:周强,美国德克萨斯大学圣安东尼奥分校市场营销学系在读博士生

演讲时间:2020年9月17日(周四)上午08:00-10:00

演讲地点:随会

文章简介:

线上纯劳力服务平台(比如Zeel, Amazon Home Services, Freelancer.com)代表了一个亿万级的市场。对于此类平台,一个突出的管理问题是服务提供者和顾客通过平台熟识后会绕开平台直接交易。我们将这个现象称为平台剥削。我们首先通过实地访谈了解为什么以及何时平台剥削现象会发生。结合访谈发现和现有的理论,我们通过实证方式探索触发平台剥削的因素。具体地,我们使用风险模型对一家连结护士和病人的医护服务平台的交易数据进行了分析。我们发现高质量护士和在平台运营较久的护士是一把双刃剑。这类护士一方面能帮助吸引病人使用线上服务平台,但是病人也更容易与这些护士达成私下协议,之后绕过平台交易。我们的分析还发现当同一护士和病人之间的平台交易增多导致信任关系建立,绕开平台交易更容易发生。此外,当护士建立自己的线下客户群后,由于精力有限,他们在平台接单的频率也有所下降。最后,以上这些效应会随着服务价格(高价格意味着较高的中介服务费),服务重复程度,以及护士在平台所接触的病人类型而有所增强或减弱。

个人介绍:

周强博士目前就读于美国德克萨斯大学圣安东尼奥分校市场营销学系。在博士学习之前,他于宁波诺丁汉大学获得国际商务管理本科学位,于英国伯明翰大学获得市场营销传播硕士学位。此外,他也曾供职于中国的金融科技公司宜信,负责区域市场营销。

周强博士的研究兴趣主要集中在跟平台市场,线上营销相关的市场营销战略。他目前的研究主要探索他称之为平台剥削的现象,也即服务提供者和顾客通过平台熟识后越过平台交易的现象。在此研究中,他主要揭示造成此问题的因素和机制,以及作为平台应该采取什么样的策略来减少此问题的发生。周强博士的其他研究议题还包括平台市场里的捆绑策略,移动app里的用户会员购买决策等。

周强博士的研究成果也赢得了学术界的广泛认可。例如,从他的博士论文产出的学术论文目前正在Journal of Marketing进行第二轮评审。他之前的研究成果也被刊登在International Journal of Advertising等国际学术期刊。此外,他的研究成果也被众多知名学术会议接收进行展示。主要的会议包括美国市场营销协会年会,消费者研究协会年会,美国经济协会年会。在2019年美国市场营销协会年会,他的展示论文也获得了领域最佳论文奖。

Abstract:

Online pure-labor service platforms (e.g., Zeel, Amazon Home Services, Freelancer.com) represent a multibillion-dollar marketplace. An increasing managerial concern in such markets is the opportunistic behavior of service agents defecting with customers from the platform, a phenomenon we call platform exploitation. We first use a theories-in-use approach to understand why and when platform exploitation occurs. Combining these insights with extant theory, we empirically explore the drivers of platform exploitation. Using data from a healthcare platform that connects nurses and patients, we develop a multi-spell hazard model and find that high-quality and long-tenured service agents are a double-edged sword. While such agents may enhance platform usage, customers are more likely to defect with high-quality or long-tenured agents. Our results also suggest that platform exploitation is most probable as same agent-customer interaction frequency increases and relationships form. We also find that exploitation decreases agents’ platform usage frequency due to capacity constraints. Furthermore, these effects are moderated by service price (as higher prices equate to more fee savings), service repetitiveness, and the makeup of the agent’s on-platform customer pool.

Qiang Zhou is currently a PhD candidate in marketing from University of Texas at San Antonio in the US. Prior to his PhD study, Qiang received his bachelor’s degree in international business management from University of Nottingham China, and master’s degree in marketing communications from University of Birmingham UK. He had also worked as regional marketing manager for finance technology company, CreditEase, in China.

Qiang’s research interest lies in marketing strategy associated with platform markets and digital marketing. His current research explores the phenomenon of platform exploitation, where service agents and customers circumvent the platform to transact. In this research, he examined factors that lead to the problem and the managerial intervention that could combat this problem. His other research projects examine topics such as bundling strategy in platform markets, consumer enrollment decision in mobile apps etc.

Qiang’s research has won considerable recognitions. For example, his dissertation essay one is now under second round review at Journal of Marketing. His previous research also appears in international journals such as International Journal of Advertising. In addition, he presented his research in several premier international conferences, such as Annual Conference of American Marketing Association (AMA), Annual Conference of the Association for Consumer Research (ACR), Annual Meeting of American Economic Association (AEA), and won the Award of Best Paper in Track at 2019 AMA.