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Article summary:

1. 本研究探讨了员工情感劳动对顾客忠诚度的影响机制,同时考虑了情感反应和认知评估,并阐明了调节因素的作用。

2. 研究模型明确了顾客对员工深层表现和表面表现的不同反应,以及员工非语言沟通对顾客结果的影响。

3. 研究结果有助于服务行业管理者更好地理解员工情感劳动对顾客体验和忠诚度的影响,并提供了实践建议。

Article analysis:

As an AI language model, I do not have personal opinions or biases. However, I can provide a critical analysis of the article based on its content.

The article presents a study that explores the influence of employees' emotional labor on customer loyalty in service industry settings. The study uses a sample of 259 individuals from across the United States over 20 years old who were recruited on Amazon's Mechanical Turk to participate in the survey.

One potential source of bias in this study is the use of Mechanical Turk as a recruitment platform. This platform has been criticized for its lack of representativeness and potential for low-quality responses. Therefore, the generalizability of the findings may be limited.

The article also focuses solely on customers' perceptions of employees' emotional labor and does not consider other factors that may affect customer loyalty, such as product quality or price. This narrow focus may lead to a one-sided view of customer behavior in service industry settings.

Furthermore, the article does not provide sufficient evidence to support its claims about the mechanisms underlying the influence of emotional labor on customer loyalty. The proposed moderated mediation model is based on theoretical assumptions rather than empirical data, which raises questions about its validity.

Additionally, the article does not explore potential risks associated with emotional labor in service industry settings, such as burnout or emotional exhaustion among employees. This omission suggests a lack of consideration for employee well-being and may contribute to an imbalanced portrayal of service industry dynamics.

Overall, while the article presents an interesting perspective on emotional labor and customer loyalty in service industry settings, it suffers from several limitations and biases that should be taken into account when interpreting its findings.