Date of Award
Summer 2018
Document Type
Dissertation
Degree Name
Doctor of Business Administration (DBA)
Department
Marketing and Analysis
First Advisor
Bruce Alford
Abstract
This research aims to investigate the influential nonverbal signals of frontline employees on customer outcomes. Frontline employees play a vital role in initiating and maintaining customer relationships. The interactions between customers and employees influence not only the immediate reactions, including both affective and cognitive responses, but also customer outcomes, like purchase intention, satisfaction, perceived service quality, and positive word-of-mouth. Both qualitative and quantitative methodologies are employed in this dissertation.
Previous studies examined the effects of employee nonverbal signals on customers’ cognitive responses, but limited research has been done on the affective responses of customers. Affect-based trust, positive affect, negative affect, and rapport are measured in this research to capture the emotional responses of customers during interactions with employees. This research gives an integrated review of the literature on nonverbal signals. The qualitative study, using semi-structured interviews, provides the fundamental elements for the experimental design. The results of the qualitative study also answer the research questions and address the importance of nonverbal signals during interactions. Four sets of nonverbal signals are used to test the proposed hypotheses. The results of this study show the effect of employee nonverbal signals on social judgments (warmth and competence), affect-based trust, and negative emotions. These immediate responses further influence customer outcomes.
This research provides an integrated review of nonverbal communication literature in marketing, investigates the importance and influence of nonverbal signals using both qualitative and quantitative methods, and proposes future research opportunities.
Recommended Citation
Wu, Shuang, "" (2018). Dissertation. 33.
https://digitalcommons.latech.edu/dissertations/33