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Digital emotional labor: Benefits and challenges of emotional labor in the context of text-based service
Arik Cheshin1, Ella Glikson2, Einat Lavee1
1Department of Human Services, Faculty of Social Welfare and Health Sciences, University of Haifa.
Abstract:
The existing emotional labor literature has traditionally focused on face-to-face or voice-to-voice customer service interactions. However, as text-based service exchanges have become increasingly common-and, for many customers, preferred-new research questions have emerged. Specifically, how does emotional labor unfold in text-based communication between customer service representatives (CSRs) and customers? And, relatedly, do these interactions involve different emotional labor strategies compared to traditional ones? Using qualitative inductive methods, including observations of service centers and interviews with CSRs and their managers, we employed grounded theory to establish how text-based exchanges align with and diverge from traditional emotional labor assumptions. Our findings reveal that text-based service significantly alters the work of CSRs, presenting both benefits and new challenges. For example, while emotions remain central, they are experienced in a more subdued manner in text-based service. Moreover, the ability to rely on prewritten messages, revise responses mid-interaction, and convey emotions easily through text shifts emotional expressions to be more cognitive and less effective, often appearing as robotic. Consequently, CSRs face a novel challenge: demonstrating they are real people (i.e., not automated chatbots) and laboring to rehumanize themselves to customers. Thus, text-based service significantly alters emotional labor, while some challenges are alleviated, new ones emerge, suggesting classical deep-acting and surface-acting concepts may not fully apply. We identify two distinct forms of digital emotional labor-robotic acting and rehumanization. Implications for theory and practice tied to emotional labor are discussed. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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