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When Language Matters
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AI
DOI：10.1093/jcr/ucad080.png)
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摘要
En 中文
Text analysis is increasingly used for consumer and marketing insight. But while work has shed light on what firms should say to customers, when to say those things (e.g., within an advertisement or sales interaction) is less clear. Service employees, for example, could adopt a certain speaking style at a conversation's start, end, or throughout. When might specific language features be beneficial? This article introduces a novel approach to address this question. To demonstrate its potential, we apply it to warm and competent language. Prior research suggests that an affective (i.e., warm) speaking approach leads customers to think employees are less competent, so a cognitive (competent) style should be prioritized. In contrast, our theorizing, analysis of hundreds of real service conversations from two firms across thousands of conversational moments (N = 23,958), and four experiments (total N = 1,589) offer a more nuanced perspective. Customers are more satisfied when employees use both cognitive and affective language but at separate, specific times. Ancillary analyses show how this method can be applied to other language features. Taken together, this work offers a method to explore when language matters, sheds new light on the warmth/competence trade-off, and highlights ways to improve the customer experience.
Keyword:
language
communication
dynamics
warmth and competence
customer service
期刊
IF:
6.4
论文数:
2.5K
被引数:
2.5W
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