Search Results (127)

In today’s competitive environment of online service industries, particularly e-commerce, meeting consumer expectations is essential for service providers to ensure service quality. However, service failures are unavoidable, leading to unfavorable consequences for businesses. Understanding the mechanisms for customer recovery after negative service experiences is crucial. Using cognitive–emotional personality systems theory and benign violation theory, this study constructed a theoretical model. A total of 351 samples were collected through a situational simulation experiment for a linear regression analysis. A self-mocking response strategy positively influenced brand trust through perceived brand authenticity regarding the dimensions of credibility, integrity, and symbolism. Simultaneously, brand trust was identified as a key driver of post-recovery satisfaction. This study proposes a chain mediation model, which incorporates perceived authenticity and brand trust, to fully comprehend the mechanisms underlying consumers’ satisfaction after service recovery. Our findings provide empirical evidence for the effects of self-mockery on post-recovery satisfaction, as well as suggestions for marketers seeking efficient means to meet consumers’ emotional and cognitive demands during service recovery situations. Full article

Emotion recognition by wearable devices is essential for advancing emotion-aware human–computer interaction in real life. Earphones have the potential to naturally capture brain activity and its lateralization, which is associated with emotion. In this study, we newly introduced tympanic membrane temperature (TMT), previously used as an index of lateralized brain activation, for earphone-based emotion recognition. We developed custom earphones to measure bilateral TMT and conducted two experiments consisting of emotion induction by autobiographical recall and scenario imagination. Using features derived from the right–left TMT difference, we trained classifiers for both four-class discrete emotion and valence (positive vs. negative) classification tasks. The classifiers achieved 36.2% and 42.5% accuracy for four-class classification and 72.5% and 68.8% accuracy for binary classification, respectively, in the two experiments, confirmed by leave-one-participant-out cross-validation. Notably, consistent improvement in accuracy was specific to models utilizing right–left TMT and not observed in models utilizing the right–left wrist skin temperature. These findings suggest that lateralization in TMT provides unique information about emotional state, making it valuable for emotion recognition. With the ease of measurement by earphones, TMT has significant potential for real-world application of emotion recognition. Full article

This study highlights the importance of managing and analyzing customer reviews to gain a competitive advantage and improve customer experience in the hospitality industry. In this context, a deep learning-based sentiment analysis system of hotel customer reviews is developed to evaluate service quality within the scope of sustainable tourism. The study analyzed 15,522 customer reviews of five-star hotels in Antalya using text mining, topic modelling, and deep learning-based sentiment analysis. The reviews were classified as positive, negative, or neutral. The findings show that Hotel HB2 has the highest performance, with an F1 score of 97.9%. Overall customer satisfaction is 91%, while emotional satisfaction stands at 77%. Key factors, such as cleanliness, food quality, and staff professionalism, were found to play a critical role in customer loyalty. Additionally, this study integrates sustainability-orientated themes by identifying customer feedback related to environmentally friendly practices and sustainable hotel operations. The results provide evidence that customer satisfaction is not only influenced by service quality but also by the perceived environmental and social responsibility of the hotel. Machine learning techniques have emerged as effective tools for analyzing large-scale customer reviews, offering valuable insights to rapidly and accurately capture customers’ emotions, expectations, and perceptions. As a comprehensive application of sentiment analysis and text mining, this research offers hotel managers a practical framework to enhance service quality, foster customer loyalty, and develop sustainability-orientated strategies. This study contributes to the literature by linking AI-driven sentiment analysis with sustainability practices in the tourism sector. Full article

The widespread deployment of service robots in industries such as hospitality has significantly transformed service delivery, influencing not only customers but also employees. This study examines the multi-dimensional impact of service robots on hotel employees, focusing on their attitudes, emotional responses, and willingness to collaborate, as shaped by perceived benefits (service reliability, process efficiency, and job crafting) and risks (inefficiency, insufficient intelligence, and privacy concerns). Data were collected from 471 hotel employees in South Korea with experience working alongside service robots, and Hayes’ Process Macro Model 4 was employed for hypothesis testing. The findings reveal that perceived benefits positively influence employees’ attitudes, emotions, and willingness to collaborate, while perceived risks exert a negative impact. Furthermore, attitudes and emotional responses mediate these relationships. These findings provide theoretical and practical insights for managers, policymakers, and service robot manufacturers to address employee concerns, improve human–robot collaboration, and promote sustainable technological integration within the service industry. Full article

Premium fashion brands are increasingly adopting Generative Artificial Intelligence (GenAI) to reduce costs and enhance creativity. However, consumers have mixed perceptions of clothing with AI-generated patterns (CAGPs) launched by premium fashion brands, especially in online shopping contexts where consumers cannot examine physical products firsthand. This study integrates the Theory of Planned Behavior (TPB) with Customer Perceived Value (CPV) to investigate Chinese Millennials’ attitudes and purchase intentions toward online purchases of CAGPs launched by premium fashion brands. Using a purposive sampling approach, the study collected 471 valid responses from Chinese Millennials. Structural equation modeling (SEM) was then employed to test the proposed model and hypotheses. The results reveal that perceived brand design effort and perceived price value are primary drivers of purchase intention for CAGPs from premium fashion brands, while perceived aesthetic value significantly shapes consumer attitudes. The subjective norm and attitude positively influence purchase intention. This study sheds light on the roles of aesthetic, emotional, monetary and social factors in driving purchase intention, offering practical suggestions for premium brands’ product design and marketing strategies. Full article

This research aims to investigate, using the C-A-B theory, the buying decision-making processes of Gen Z consumers in the United States when exposed to fast fashion brand advertising messages including greenwashing elements. Responses of 345 valid participants from the Amazon Mturk platform were analyzed through Mplus 8.11 and SPSS 29. Two-step, structural equation modeling was implemented to test the hypothesis. Additionally, 5000 bootstrapping iterations were used to examine the indirect effects. Study findings indicated that Gen Z consumers responded positively and negatively to fast fashion brands’ product promotional messages. Despite feeling skeptical and betrayed over the greenwashing assertion, they intend to purchase the goods. A contributing factor to this unforeseen purchasing intention may be their indifference towards environmental concerns. Moreover, when greenwashing assertions are infused with product advantages through strategic ingenuity and aligned with the specific demands of certain generations, the perception of positive emotional reaction supersedes the negative, hence facilitating the purchase of the green product. Furthermore, there is evidence of optimism biases, a cognitive bias where they exaggerate their capacity to identify instances of greenwashing, prioritize more on their certain needs, and underestimate the associated environmental risk for others. This clarifies the paradoxical buying patterns of Gen Z consumers. Although Gen Z is the youngest demographic, their tastes for fast fashion apparel may alter as they develop and their lifestyles adapt, influenced by both positive and negative emotional reactions to fast fashion brands. Consequently, the fast fashion business must retain this customer by utilizing sustainability messaging instead of misleading greenwashing assertions in the future. Full article

Previous discussions on customer injustice and emotional labor have primarily focused on employee–customer dyads, often neglecting the role of service co-workers in shaping emotional labor dynamics. To address this gap, the current study integrates intrapersonal and interpersonal factors to explore their joint effects on employees’ emotional labor strategies when encountering customer injustice. A full-factorial experimental design with 2 (self-construal: independent vs. interdependent) × 3 (service co-workers: alone vs. positive attitudes vs. negative attitudes toward customer injustice) is employed, using data from 179 frontline service employees at high-star hotels in Shanghai, with self-construal and service co-workers operationalized as manipulated conditions. Results reveal that self-construal significantly influences surface acting: interdependent individuals are more inclined to engage in surface acting than independent individuals. By contrast, self-construal has no direct effect on deep acting. While service co-workers do not moderate the relationship between self-construal and surface acting, they play a critical role in the relationship between self-construal and deep acting: for interdependent employees, service co-workers’ attitudes (rather than their mere presence) decisively impact deep acting, with positive attitudes promoting deeper emotional engagement and negative attitudes reducing it. This study advances a dual-path framework highlighting how intrapersonal dispositions (self-construal) and interpersonal impression cues (service co-workers’ attitudes) interact to shape emotional labor. By expanding the traditional employee–customer dyad to a triadic model, the study bridges impression management theory and workplace injustice research, offering theoretical insights into how intrapersonal traits and interpersonal dynamics jointly shape contextualized emotional labor. This thereby provides a theoretical foundation for nuanced management strategies in service organizations. Full article

The analysis of human movement is crucial in biomechanical research and clinical practice. Quantitative movement analysis evaluates sports performance by tracking joint angles, segmental velocities, and body positions. There are high-accuracy motion-tracking systems like Vicon Motion Systems (Oxford, UK) or OptiTrack (Corvallis, OR, USA), but they are expensive, require expertise, and lack portability. This study assessed a low-cost virtual reality-based motion-tracking system with a customized eMotion data acquisition and analysis application to describe joint movements during squatting. The system, which utilizes commonly available virtual reality accessories, successfully collected kinematic data and continuous tracker trajectories. The results showed high repeatability comparable to advanced optoelectronic motion-capture systems. The eMotion system protocols exhibited low variability for most rotations, with inter-trial values ranging from 0.65° to 2.20° except for hip and knee flexion, which reached 3.09° and 4.01°. The motion-tracking technology that is part of VR headsets has great potential in supporting training and rehabilitation by enabling quantitative assessment of any activity in both the real and virtual worlds. The use of low-cost solutions can increase the potential for human motion measurements in clinical practice and biomechanical research. Full article

Cultural factors shape students’ expectations and perceptions within diverse educational settings. The perceived quality of a Higher Education Institution (HEI) is crucial to its success, with student satisfaction determined mainly by their overall experiences. The concept of Student eXperience (SX) can be analyzed through the lens of Customer eXperience (CX) from a Human–Computer Interaction (HCI) perspective, positioning students as the “customers” of the institution. SX encompasses academic and physical interactions and students’ emotional, social, and psychological responses toward an institution’s systems, products, and services. By accounting for factors such as emotions, socioeconomic status, disabilities, and, importantly, cultural background, SX provides a comprehensive measure of student experiences. Building upon our previous SX model and Hofstede’s national culture model, we have developed a Student eXperience evaluation methodology that serves as a diagnostic tool to assess both student satisfaction and how effectively HEIs serve a diverse student population. This methodology ensures that all students, regardless of their background, are considered in the evaluation process, facilitating the early identification of institutional strengths and weaknesses. Incorporating cultural aspects into the assessment delivers more precise results. Furthermore, our approach supports HEIs in promoting equity, diversity, and inclusion by addressing the needs of minority students and students with disabilities, as well as reducing gender disparities. These objectives align with UNESCO’s Sustainable Development Goals, contributing to fostering an equitable learning environment. By adopting such inclusive evaluation practices, HEIs can enhance the perceived quality of education and their responsiveness to the needs of an increasingly multicultural student body. Full article

Emotion regulation (ER) strategies, such as reappraisal and suppression, have been linked to psychological well-being. The available evidence points to the differential impact of ER strategies on resilience and post-traumatic growth (PTG), as factors related to well-being, as well as to sex differences in the link between ER preference and well-being. However, previous studies are mixed regarding these links. To address this issue, college students (N = 1254) recruited between 2020 and 2023 reported their habitual use of ER strategies, resilience and PTG during the COVID-19 pandemic, which, as a global health crisis, has raised not only severe physical health concerns but also mental distress. First, reappraisal was positively associated with both resilience and PTG, whereas suppression was negatively correlated with these measures. Second, female participants had lower suppression scores and higher PTG scores than male participants. Third, a moderation analysis showed that the positive relationship between reappraisal and PTG was stronger in female participants, whereas the negative relationship between suppression and PTG was stronger in male participants. Overall, these findings shed light on the links among ER strategies, resilience, and PTG and have relevance for customized training in the use of reappraisal to increase well-being in women and men. Full article

This study examines how the use of postural and mobility devices evolves in home environments for children with GMFCS IV cerebral palsy, focusing on parents’ perspectives on benefits, outcomes, and challenges. As children grow, changes in muscle strength, motor function, and daily activity demands necessitate adjustments in assistive devices to maintain mobility and postural support. Data from 10 parents, collected through descriptive statistics and qualitative interviews, covered device types, usage patterns, and family impacts across developmental stages from preschool to adulthood. Device needs shift significantly with growth, transitioning from early gait trainers and postural support devices to advanced mobility devices, such as power wheelchairs, which become essential in adulthood. Parents reported positive outcomes, including improved emotional well-being, social participation, and independent mobility, alongside reduced caregiving burdens. However, challenges persist, including financial constraints, frequent device replacements, and limited training for users and caregivers. These insights highlight the need for more adaptable device designs and enhanced family-centered support programs to better assist caregivers in managing device transitions. This study addresses a gap by exploring the real-world outcomes of home-based device use, providing data and parental insights to inform device design, clinical practices, and family-centered support programs. Future research should focus on enhancing device functionality, customization, and accessibility to improve quality of life and promote greater independence for individuals with cerebral palsy. Full article

This study explores the impact of work stress on compensatory tourism consumption, with work stress categorized into challenge stressors and hindrance stressors. The research examines the potential for individuals to engage in regular compensatory tourism consumption. Grounded in the Challenge-Hindrance Stress framework, compensatory consumption theory, and the Stimulus-Organism-Response (S-O-R) model, the study employs structural equation modeling (SEM) to integrate these theories with the research model. Data were collected through an online survey, yielding 375 responses, of which 361 were valid. The sample was subjected to statistical analysis of sample characteristics, multifactorial analysis of variance, correlation analysis, convergent validity analysis, regression analysis, and Bootstrap mediation effect testing. The results indicate that work stress is positively correlated with compensatory tourism consumption, and emotional exhaustion partially mediates this relationship. Therefore, tourism industry enterprises can focus on maintaining relationships with customers who choose compensatory tourism consumption, thereby increasing the likelihood of sustainable, recurring compensatory tourism consumption among these customers. Full article

This study investigated the impact of garden-based learning on young adults with intellectual disabilities (YAwID). Since YAwID often experiences difficulties collecting information, experiencing situations, and making decisions independently, we developed and implemented a customized learning program for them and analyzed its impact. This program was devised specially to help YAwID utilize the garden’s resources properly. The findings showed that garden-based learning generated specific changes in the knowledge, skills, and attitudes of YAwID, such as emotional healing to recover psychological stability, social healing through social relationships, physical healing through new activities, and independent healing through individualized plans. Participants experienced sympathizing with others and maintaining positive relationships and obtained the knowledge, skills, and attitudes necessary for behaving responsibly through gardening. Garden-based learning utilizing flowers and plants enhanced participants’ physical and mental health, improved their functioning, and helped them adapt to the environment and integrate into society. Our program can be considered a form of vocational training by which people with disabilities can enhance their employability by learning gardening skills, promoting their participation in society, and improving their quality of life. Hence, garden-based learning may transform people’s perception of disability and help people with disabilities respond to challenges. Full article

Spices and aromatic herbs are important components of everyday nutrition in several countries and cultures, thanks to their capability to enhance the flavor of many dishes and convey significant emotional contributions by themselves. Indeed, spices as well as aromatic herbs are to be considered not only for their important values of antimicrobial agents or flavor enhancers everybody knows, but also, thanks to their olfactory and gustatory spectrum, as drivers to stimulate the consumers’ memories and, in a stronger way, emotions. Considering these unique characteristics, spices and aromatic herbs have caught the attention of consumer scientists and experts in sensory analysis for their evaluation using semi-quantitative approaches, with interesting evidence. In this pilot study as a first step, each studied botanical, belonging to Piperaceae or aromatic herbs, has been subjected to headspace solid phase micro-extraction (HS-SPME) coupled with gas-chromatography mass spectrometry (GC-MS) analysis to assess their spontaneous volatile emission, representing the complex chemical pattern, which encounters the consumers’ olfactory perception. Furthermore, the present investigation, performed on 12 individuals, outlines the administration of a pilot study, merging the typical sensory analysis with emotional data collection and the innovative contribution related to the study around the Autonomic and Central Nervous System activation in consumers, performed using wearable technologies and related signal processing. The results obtained by our study, beyond demonstrating the feasibility of the approach, confirmed, both in terms of emotional responses and biomedical signals, the significant emotional potential of spices and aromatic herbs, most of which featuring an overall positive valence, yet with inter-subjects’ variations. Future investigations should aim to increase the number of volunteers evaluated with such an approach to draw more stable conclusions and attempting a customization of product preferences based on both implicit and explicit sensory responses. Full article

Sentiment analysis via artificial intelligence, i.e., machine learning and large language models (LLMs), is a pivotal tool that classifies sentiments within texts as positive, negative, or neutral. It enables computers to automatically detect and interpret emotions from textual data, covering a spectrum of feelings without direct human intervention. Sentiment analysis is integral to marketing research, helping to gauge consumer emotions and opinions across various sectors. Its applications span analyzing movie reviews, monitoring social media, evaluating product feedback, assessing employee sentiments, and identifying hate speech. This study explores the application of both traditional machine learning and pre-trained LLMs for automated sentiment analysis of customer product reviews. The motivation behind this work lies in the demand for more nuanced understanding of consumer sentiments that can drive data-informed business decisions. In this research, we applied machine learning-based classifiers, i.e., Random Forest, Naive Bayes, and Support Vector Machine, alongside the GPT-4 model to benchmark their effectiveness for sentiment analysis. Traditional models show better results and efficiency in processing short, concise text, with SVM in classifying sentiment of short length comments. However, GPT-4 showed better results with more detailed texts, capturing subtle sentiments with higher precision, recall, and F1 scores to uniquely identify mixed sentiments not found in the simpler models. Conclusively, this study shows that LLMs outperform traditional models in context-rich sentiment analysis by not only providing accurate sentiment classification but also insightful explanations. These results enable LLMs to provide a superior tool for customer-centric businesses, which helps actionable insights to be derived from any textual data. Full article