Emotions embody the value in tourism experiences and drive essential outcomes such as intent to recommend. Current models do not explain how the ebb and flow of emotional arousal during an experience relate to outcomes, however. We analyzed 15 participants’ experiences at the Vincentre museum and guided village tour in Nuenen, the Netherlands. This Vincent van Gogh-themed experience led to a wide range of intent to recommend and emotional arousal, measured as continuous phasic skin conductance, across participants and exhibits. Mixed-effects analyses modeled emotional arousal as a function of proximity to exhibits and intent to recommend. Experiences with the best outcomes featured moments of both high and low emotional arousal, not one continuous “high,” with more emotion during the middle of the experience. Tourist experience models should account for a complex relationship between emotions experienced and outcomes such as intent to recommend. Simply put, more emotion is not always better.
Understanding the complex and dynamic nature of experiences requires the use of proper measurement tools. As interest grows in the objective measurement of experiences within tourism and hospitality, there is an urgent need to consolidate and synthesize these studies. Thus, this study investigated prevalent objective measurement techniques via a systematic review. We analyzed physiological measures such as electroencephalography (EEG), heart rate variability (HRV), skin conductance (SC), and facial electromyography (fEMG) along with behavioral measures, including eye tracking and location tracking. This review identified 100 empirical studies that employed objective measurement to examine tourism and hospitality experiences over the last decade, highlighting trends, research contexts and designs, and the synergies between different methods. Our discussion on methodological issues and best practices will help researchers and practitioners identify the best tools to capture people’s experiences and promote more standardized practices and comparable findings on studying experiences in tourism and hospitality settings.
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Anxiety among pregnant women can significantly impact their overall well-being. However, the development of data-driven HCI interventions for this demographic is often hindered by data scarcity and collection challenges. In this study, we leverage the Empatica E4 wristband to gather physiological data from pregnant women in both resting and relaxed states. Additionally, we collect subjective reports on their anxiety levels. We integrate features from signals including Blood Volume Pulse (BVP), Skin Temperature (SKT), and Inter-Beat Interval (IBI). Employing a Support Vector Machine (SVM) algorithm, we construct a model capable of evaluating anxiety levels in pregnant women. Our model attains an emotion recognition accuracy of 69.3%, marking achievements in HCI technology tailored for this specific user group. Furthermore, we introduce conceptual ideas for biofeedback on maternal emotions and its interactive mechanism, shedding light on improved monitoring and timely intervention strategies to enhance the emotional health of pregnant women.