How does leader secure-base support affect hospitality employees’ service performance? Role of work engagement and role stressors

Abstract

This study proposes a conceptual framework wherein leader secure-base support affects service performance among hospitality employees through the mechanism of work engagement. Additionally, it examines the moderating roles of role ambiguity and role conflict. A time-lagged survey was administered in three hotels in China, while an experiment on airline employees from western countries was performed using the Prolific platform. Via hierarchical multiple regression, we find that leader secure-base support indirectly enhances the service performance of hospitality employees by fostering their work engagement. And this indirect effect of leader secure-base support on service performance varies with different levels of role ambiguity and role conflict. Accordingly, hospitality managers ought to aim to provide more secure-base support that provides a sense of security for their employees, and they can adopt a variety of strategies in fostering employees’ work engagement. What’s more, to leverage the positive effects of leader secure-base support, hospitality managers should provide clear and consistent role definitions and expectations. The study advances the literature on leader secure-base support and service performance both in the general management and hospitality area. Integrating a new perspective from the conservation of resources (COR) theory with the attachment theory, it further uncovers the mediating role of work engagement in the relationship between leader secure-base support and service performance. Additionally, it establishes boundary conditions by exploring the moderating effects of role ambiguity and role conflict.

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Citations
Research
Paper Only

Study specs

Year
2026
Human Data Platform
Prolific

Peer Review & Critical Discussion

3 threads

Potential Selection Bias in 2023 Cohort

DSJDr. Sarah J.
Verified PhD Candidate
12 replies

The participant pool shows a concerning overrepresentation of users from high-income demographics. Looking at Table 3, we can see that 78% of respondents had annual incomes above $75k, which significantly limits the generalizability of these findings to broader populations.

2 hours ago

Non-naive Participants Issue

MCM. Chen (OpenAI)
Data Scientist
8 replies

I've noticed a methodological concern regarding participant naivety. Given that Prolific users often complete multiple studies, there's a real risk that participants had prior exposure to similar experimental paradigms, which could confound the results.

5 hours ago

RLHF Applicability to This Study Design

PRWProf. R. Williams
Verified Researcher
15 replies

The implications for RLHF training pipelines are understated. If we accept the authors' conclusions about preference stability, this has direct consequences for how we should structure reward model training. The temporal decay effect described in Section 4.2 is particularly relevant.

1 day ago

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