EXPRESS: CUSTOMER’S MIXED EMOTIONS RESEARCH IN TOURISM AND HOSPITALITY: ANTECEDENTS, CONSEQUENCES AND A THEORETICAL FRAMEWORK

Zhu Haiyan & Shanshi Li

Journal of Hospitality and Tourism Research2026https://doi.org/10.1177/10963480261434443article
AJG 2ABDC A
Weight
0.50

Abstract

Despite increasing scholarly attention, research on mixed emotions in hospitality and tourism remains fragmented and theoretically underdeveloped. This review synthesizes 49 peer-reviewed articles to provide an integrated overview. Prior research has largely focused on external stimuli or narrowly defined cognitive evaluations as antecedents, offering limited insight into why similar contexts elicit different mixed emotional responses. Although both positive and negative outcomes have been documented, underlying mechanisms remain insufficiently theorized. Drawing on appraisal theories, we advance a framework built on three tenets: (a) mixed emotions can be classified as conflicting or additive according to appraisal-emotion patterns; (b) both individual appraisals and contextual cues jointly shape the emergence of mixed emotions; and (c) outcomes depend on appraisal tendencies, with coping strategies such as emotion-focused or problem-focused regulation mediating potential behavioral inhibition effects. We conclude by identifying key directions for future research and outlining managerial implications for tourism and hospitality.

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https://doi.org/https://doi.org/10.1177/10963480261434443

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@article{zhu2026,
  title        = {{EXPRESS: CUSTOMER’S MIXED EMOTIONS RESEARCH IN TOURISM AND HOSPITALITY: ANTECEDENTS, CONSEQUENCES AND A THEORETICAL FRAMEWORK}},
  author       = {Zhu Haiyan & Shanshi Li},
  journal      = {Journal of Hospitality and Tourism Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/10963480261434443},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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