Signaling authenticity for frontlineservice employees
Matthews, A. Lynn
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Matthews, A. L., & Eilert, M. (2021). Signaling authenticity for frontlineservice employees. Journal of Services Marketing, doi:10.1108/JSM-12-2020-0486
Purpose: Authenticity is a complex character that is valued in service contexts. Frontline service employees (FSEs), as both brand representatives and individuals who interact with clients, can signal their authenticity to customers. The purpose of this study is to investigate how FSEs signal their authenticity to customers. The authors investigate authenticity signal themes and develop a typology of how FSEs use these signals in the workplace. Design/methodology/approach: This research uses a multi-method approach: qualitative data were collected through in-depth interviews with FSE and customers and quantitative data were collected in a follow-up survey using a sample of financial planners. Findings: Findings from both studies show that FSE can use signals reflecting the display of client-centricity, positive emotions, transparency and disclosure of personal information. A latent profile analysis reveals three authenticity signal profiles, differing in the extent to which FSE uses each of these signals. Research limitations/implications: This study identifies how FSEs can shape perceptions of authenticity in a service context, thus expanding theory by integrating both personal and brand authenticity perspectives. The findings further demonstrate that authenticity can be signaled on multiple dimensions, reflecting the complex nature of this construct. Practical implications: The findings from this research can guide managers in developing workplace policies that enable FSEs to display authenticity in various ways to customers. Managers can further use the insights from this research to identify needs for FSE training and development. Originality/value: The authors create novel insights into how FSEs signal authenticity to customers given their dual roles as individuals and brand representatives. This study offers nuanced insights into different types of signals and their application in a service context.
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