Purpose This study aims to examine how COVID-related corporate social responsibility (CSR) activities affect future Generation Z employees’ intention to join the hotel industry through experimental designs. Design/methodology/approach Based on signaling theory, construal level theory and value theory, this study established an integrated research framework to explain the mechanism of CSR communication. The proposed study conducted three online experiments on a total of 463 participants. ANCOVA test and PROCESS macro were performed to analyze the data for main, mediation and moderation effects. Findings The results of this study indicate that in-kind donation is more efficacious in improving Generation Z’s job pursuit intention, as compared to cause-related marketing (CRM). CSR messages framed in a “how” mindset are favored by Generation Z members who are either unemployed or eager to change their current job. The findings also confirm the effect of brand warmth as a mediator and other-regarding personal value as a moderator. Research limitations/implications The present study contributes to the limited knowledge on CSR initiatives by addressing the research gap of future employees and examining CSR as a response to COVID-19. The findings also provide hotel executives actionable implications to plan and communicate future CSR programs, especially during times of crisis. Originality/value This study is one of the first studies to address Generation Z employees and to investigate the role of CSR initiatives on future hotel workers.

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Cables are critical components for a variety of structures such as stay cables and suspenders of cable-stayed bridges and suspension bridges. When in operational service, they are vulnerable to cumulative fatigue damage induced by dynamic loads (e.g., the cyclic vehicle loads and wind excitation). To accurately analyze and predict their dynamics behaviors and performance that could be spatially local and temporal transient, it is essential to perform highresolution vibration measurements, from which their dynamics properties are identified and, subsequently, a high spatial resolution, full-modal-order dynamics model of cable vibration can be established. This study develops a physics-guided, unsupervised machine learning-based video processing approach that can blindly and efficiently extract the fullfield (as many points as the pixel number of the video frame) modal parameters of cable vibration using only the video of an operating (output-only) cable. In particular, by incorporating the physics of cable vibration (taut string model), a novel automated modal motion filtering method is proposed to enable autonomous identification of full-order (as many modes as possible) dynamic parameters, including those weakly excited modes that used to be challenging to identify in operational modal analysis. Therefore, a full-field, full-order modal model of cable vibration is established by the proposed method. Furthermore, this new approach provides a low-cost and noncontact technique to estimate the cable tension using only the video of the vibrating cable where the fundamental frequency is automatically and efficiently estimated to compute the cable tension according to the taut string equation. Laboratory experiments on a bench-scale cable are conducted to validate the developed approach.

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