See no evil in the voice-to-voice customer service context

A sample of more than 28,000 front-line employee (FLE) - customer interactions, extrapolating from foundational framing, we pit conventional service approaches against one another to propose a dual-process model, situating customer frustration/satisfaction as mediators of the indirect relationships between resolution/relational tactics and call duration – a key customer service efficiency outcome.

Methods
In order to provide an internally valid empirical foundation to test our model, the sampling frame we adopt was based on a random sample of inbound calls received over a one-year time frame, resulting in 28,103 dyadic interactions, handled by a total of two-hundred and seventeen separate FLEs, employed at four call centers located across the US. The data on which our analyses are based was provided by a Fortune 500, nationally – branded, market-leading insurance company headquartered in the US.

To account for the nested structure of the complex data that underlie our analyses, we estimated one omnibus moderated-mediation, random-effects model in Mplus v. 8 (Muthén & Muthén, 2017). This approach allows us to take into account complex sampling features related to our panel data, including generating robust standard errors (Thompson, 2011), teasing out variations potentially caused between FLEs, thereby controlling for omitted, unobserved effects related to the ways in which the 217 FLEs handled each call (Germann et al., 2015).

本数据集包含超过28000条一线员工（front-line employee, FLE）与客户的互动样本。基于基础理论框架推演，本研究将传统服务策略两两对比，提出双过程模型，将客户不满与满意设定为解决导向策略、关系导向策略与通话时长——客户服务效率的核心结果指标——之间间接关系的中介变量。

研究方法
为构建内部有效的实证基础以检验所提出的模型，本研究采用的抽样框架基于对1年内接收的呼入电话的随机抽样，最终得到28103组二元互动样本，由分布在美国四家呼叫中心的共计217名独立一线员工完成服务对接。本研究分析所用数据由总部位于美国的《财富》世界500强全国性品牌保险巨头提供，该企业为市场领先的保险服务商。

为处理支撑本研究分析的复杂数据的嵌套结构，我们采用Mplus v.8（Muthén & Muthén, 2017）构建了整体调节中介随机效应模型。该方法可适配本研究面板数据的复杂抽样特征，包括生成稳健标准误（Thompson, 2011），分离出一线员工间可能存在的差异，从而控制因217名一线员工各自的通话处理方式所引发的遗漏未观测效应（Germann et al., 2015）。