Redefining emotional labor in ai-enhanced hospitality integrating role and identity theories with ai anxiety as a moderator

Abstract

Based on the integration of role theory, identity theory, and emotional labor theory, this study investigates the effect of role clarity, role ambiguity, role conflict, and role identity on hospitality frontline employees’ emotional labor strategies (surface acting and deep acting). The study further explores the moderating effect of AI anxiety on these relationships. Data will be collected from frontline employees working in four- and five-star hotels in Taiwan using a structured questionnaire. The proposed framework will be tested with Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings are expected to offer theoretical contribution to emotional labor literature by integrating structural (role-related) and psychological (identity-related) perspectives. The study also provide implications for hospitality managers with insights into managing emotional challenges and technology-related stress in AI- enhanced service contexts. Research purpose: This study aims to develop an integrated framework that explains how role perceptions (clarity, ambiguity, conflict) and role identity shape frontline hospitality employees’ emotional labor, with AI anxiety as a contextual moderator. By linking role theory, identity theory, and emotional labor theory, the study offers better understanding of managing emotions in increasingly AI-mediated workplaces. Research motivation: Although emotional labor has been extensively studied in hospitality, existing research predominantly focuses on role stressors such as ambiguity and conflict, with little attention to the effect of role identity on emotional regulation. At the same time, the adoption of AI is changing the workplace rapidly, however, AI anxiety remains largely overlooked in emotional labor models. Therefore, to addresses these gaps, this study proposes a theoretical framework with the integration of identity-based and role-based perspectives and examining AI anxiety as a moderator influencing emotional labor outcomes. Research design, approach, and method: This study employs a quantitative, survey-based design and uses a purposive sampling method to ensure respondents actively interact with guests in various roles in hotels. Data will be collected through both online and paper-based questionnaires. The study will use PLS-SEM with SmartPLS 4.0 to test the model and hypotheses. Main findings: The study is expected to validate (1) the effect of role clarity, role ambiguity, role conflict, and role identity on emotional labor and (2) the moderating effect of AI anxiety on these relationships by intensifying the negative effects of ambiguity and conflict and weakening the positive influence of clarity and identity. Practical/managerial implications: The findings will provide implications for hospitality managers. Clarifying job expectations, reducing ambiguity and conflict, and strengthening role identity can encourage authentic emotions and reduce burnout. Additionally, acknowledging AI anxiety can help hotel managers prepare proactive interventions such as improving transparent AI communication and provide regular AI-training programs for employees. Such initiatives can help employees maintain emotional authenticity and service quality in AI-enhanced hospitality environments.