High Quality Of Experience Research Articles

Does Festival Quality Effect Psychological Well-being Through Festival Value and Festival Satisfaction?

This study investigates the impact of festival quality on the psychological well-being of attendees at The Eras Tour in Indonesia, emphasizing the mediating roles of festival value and satisfaction. The background highlights the increasing significance of large-scale music events, which necessitate high-quality experiences to enhance attendee satisfaction and well-being. The research aims to explore how festival quality influences psychological well-being through perceived value and satisfaction. A questionnaire was administered to 190 Indonesian respondents who attended the concert, using a 5-point Likert scale for measurement. The analysis employed Partial Least Squares Structural Equation Modelling (PLS-SEM) to evaluate the relationships among variables. Results indicate that festival quality positively affects both festival value and psychological well-being, confirming that higher quality leads to greater perceived value and emotional responses. While festival satisfaction did not significantly mediate this relationship, festival value emerged as a crucial mediator. These findings suggest that enhancing festival quality can improve attendees' psychological well-being, offering practical implications for event organizers to focus on aspects such as venue selection and service quality to foster a more satisfying experience. Future research should explore the long-term effects of these variables and develop validated measurement scales for better empirical comparisons.

Towards a better understanding of customer satisfaction with shopping mall food courts

PurposeIn this study, we examined the attributes that determine shoppers’ satisfaction with food courts in shopping malls and the effects of this satisfaction on behavioural intentions towards shopping malls.Design/methodology/approachThe data for this study were collected from 380 shoppers who visited food courts in shopping malls located in Hyderabad, India. The data were analysed using structural equation modelling.FindingsThe results indicate that fair prices, authenticity and the quality of the food, service and environment predict shoppers’ dining satisfaction and behavioural intentions. The findings also indicate that satisfaction with food court experiences has a strong positive influence on shoppers’ intention to revisit shopping malls.Practical implicationsFood service providers in Indian shopping malls should recognise that offering suitably priced and high-quality experiences is vital for sustaining their businesses in the long term as well as offer prompt service and warm hospitality to visitors to enhance customer satisfaction.Originality/valueThis study has implications for mall operators and food court managers seeking to enhance customer satisfaction with food courts and sustain the success of shopping malls. To the best of our knowledge, this is the first study to examine the factors that influence shoppers’ dining satisfaction and behavioural intentions in this context.

Volunteer Voices: building volunteer agency in the field of contemporary art in heritage

ABSTRACT Volunteers are a key but underexploited resource in advocating for and interpreting the contemporary artworks that are increasingly being commissioned for heritage places. Volunteers are not typically offered any training to help them talk about contemporary artworks with visitors or provided with much background information about the art or artists concerned. Nor is the organisation’s rationale for engaging with contemporary art well-understood by volunteers. While heritage organisations rely heavily on volunteers to provide high quality experiences for their visitors, in failing to adequately prepare volunteers, the full value of investment in a contemporary art project – time, money, organisational input, the creative energy of the artist – might not be properly realised. In this article, which draws on findings from a pilot volunteer training programme conducted in North East England in 2021–2022, we develop existing research into ‘serious leisure’ to suggest the value of building a community of practice through peer-learning that can offer a distinctive experience for volunteers, enhancing both their ownership and agency. We argue for the benefits of knowledge exchange across heritage sites and suggest that volunteers could be at the forefront in improving commissioning and exhibiting practices and visitor appreciation of contemporary art in heritage places.

Behavioral Intentions in Sports Tourism: Meta-Analysis and Structural Equation Modeling

This paper addresses fragmentation in sports tourism research, specifically concerning factors influencing behavioral intentions, focusing on key psychological and situational variables. Through a meta-analysis of 18 studies comprising 14,495 observations, drawn from an initial screening of 967 articles listed in the Scopus database, this study investigates the effects of perceived behavioral control, subjective norms, nostalgia, satisfaction, percieved benefit, and percieved risk, on behavioral intentions among general sport tourists. Two distinct theoretical frameworks were examined to uncover the core drivers of intentions. In the first framework, the analysis revealed that perceived behavioral control (effect size = .58), subjective norms (effect size = .50), and nostalgia (effect size = .43) have a stronger influence on behavioral intentions than attitude or image. This finding suggests that the participatory and socially embedded nature of sport tourism amplifies the impact of practical and social factors. Nostalgia emerged as a particularly strong emotional motivator, as it fosters attachment to past sport experiences and enhances future engagement. Among the components of the Theory of Planned Behavior, perceived behavioral control highlights the significance of practical considerations, such as ease of access and affordability, in shaping tourists’ intentions. The second framework underscored satisfaction as the most influential predictor of behavioral intentions (effect size = .77), consistent with Expectancy-Disconfirmation Theory, where experiences that meet or exceed expectations lead to future engagement. Perceived benefit (effect size = 0.50) also played a significant role, indicating that anticipated rewards, such as enjoyment and social connections, positively affect decision-making. In contrast, perceived risk (effect size = -0.25) showed a negative effect on behavioral intentions, in line with Prospect Theory, which emphasizes how potential losses deter engagement, particularly in unfamiliar or high-risk settings. These findings have practical implications for sport tourism management. Enhancing nostalgia, facilitating easy access, and fostering social connections can significantly boost behavioral intentions. Additionally, strategies that prioritize high-quality experiences to maximize satisfaction, while emphasizing perceived benefits and minimizing perceived risks through transparent safety measures, can further encourage positive engagement. Future research should explore specific conditions, such as familiar destinations, group dynamics, and satisfaction-driven contexts, that might enhance the influence of these key factors.

Wellness tourism destination loyalty formation: the role of mental health and eudaimonic well-being

PurposeWellness tourism is growing in importance as increasing numbers of travelers place a priority on their health and well-being by traveling. This study examined the relationships between wellness tourism destination attributes, perceived quality, perceived mental health, eudaimonic well-being, overall satisfaction and behavioral loyalty to corroborate a model explaining wellness tourism destination loyalty in Thailand.Design/methodology/approachThe conceptual model of this study was examined using a survey research design. The survey questionnaire was distributed to Chinese tourists who had previously traveled to Thailand and engaged in wellness tourism activities during their trip.FindingsThe findings revealed that healthful food choices, core facilities and staff service significantly influenced perceived quality. This perceived quality was a crucial factor in determining perceived mental health, which in turn impacted eudaimonic well-being. Overall satisfaction was directly influenced by perceived quality, perceived mental health and eudaimonic well-being. Additionally, perceived quality had a direct effect on behavioral loyalty.Practical implicationsWith the growing global interest in wellness and travel, this study offers valuable insights for tourism marketers in Thailand to enhance their wellness tourism strategies. Tourism organizations should emphasize the quality of food, facilities and staff service to attract wellness-oriented travelers.Originality/valueThis study highlights the interconnectedness of perceived quality, good mental health and eudaimonic well-being. High-quality experiences contribute to improved mental health and in turn enhance eudaimonic well-being.

Ten Years of Operating a Center with Large-Scale Virtual-Reality Installations: Developments and Learnings

Abstract Virtual reality (VR) can be found in many fields including art history, education, research, and industry 4.0. Next to lightweight head-mounted displays there are also VR installations, such as immersive 3D screens, large-scale displays, and CAVE systems, that are used in research, scientific visualization, and also the automotive industry. These systems offer high visual quality and collaborative VR experiences for researchers and have long been used in research. We present learnings and insights from ten years of operating and maintaining a visualization center with large-scale immersive displays and installations. Our report aims to answer questions on the benefits and challenges of such a location-based VR center. We broke down the ten years into three phases and discuss the installations themselves, software, and other various developments of the center over time, illustrated with exemplary use cases. Finally, we cover our experiences at the beginning of phase four with the installation of a (novel) LED CAVE and the future (forward-looking) developments we expect for location-based VR centers.

Implementing an Environmental Contaminants Deliberation Module in General Chemistry.

Placing chemistry in the context of complex societal issues is one way to help students see the application of fundamental ideas in the general chemistry curriculum. Here, we describe the impact of an in-class deliberation on environmental contaminants, which encourages students to consider different perspectives when addressing the issue of water and soil quality in communities. Student surveys were used to analyze the quality of the deliberation and several key factors regarding student attitudes before and after the activity. Students report a high-quality experience during the deliberation, wherein new ideas were introduced and they carefully considered different views on the issue at hand. Not only do students gain scientific knowledge about lead contamination, they also demonstrate statistically significant gains in their attitudes toward chemistry and their motivation to take action. As a complement to traditional teaching methods, this deliberation module can address key learning outcomes in systems thinking and the impact chemistry has on society.

Cognitive, affective and behavioural repercussions of perceived quality in tourism vocational education: insights from self-determination theory

PurposeThis study aimed to investigate the influence of the perceived quality by students regarding their experiences in vocational schools in tourism, hospitality and food service on cognitive and affective responses (satisfaction, self-efficacy expectations and self-regulation strategies of motivation for learning - SRSML) and commitment (behavioural response).Design/methodology/approachA cross-sectional survey was conducted with 454 students from twelve Turismo de Portugal IP-affiliated schools. The theoretical framework was rooted in Self-Determination Theory (SDT) and PLS-SEM, using ADANCO 2.3 software to test the proposed model.FindingsPerceived quality by students concerning their school experiences are socio-contextual factors that directly influence their satisfaction and self-efficacy expectations (cognitive and affective responses). Satisfaction and self-efficacy expectations positively affected SRSML. Finally, satisfaction and the SRSML positively impacted the students` commitment to schools (behavioural response).Originality/valueThis study contributes to academia by comprehensively addressing 16 perceived quality dimensions within vocational education. It aligns with SDT, revealing that socio-contextual factors affect students’ cognitive, affective, and behavioural responses. Additionally, it demonstrates positive relationships between student satisfaction, self-efficacy expectations, SRSML, and students’ commitment to vocational education institutions. This study emphasises the multidimensional nature of perceived quality, urging educational institutions to address tangible and intangible dimensions to develop strategies to provide high-quality experiences, increasing students` satisfaction, self-efficacy expectations, motivation, and commitment.

Experiences and lessons learned from a patient-engagement service established by a national research consortium in the U.S. Veterans Health Administration.

Meaningful engagement of patients in the research process has increased over the past 20 years. Few accounts are available of engagement infrastructure and processes used by large research organizations. The Pain/Opioid Consortium of Research (Consortium) is a U.S. Department of Veterans Affairs (VA) research network that provides infrastructure to accelerate health research and implementation of evidence-based health care. The Consortium's key activities include facilitating Veteran-engaged research and building community between Veterans and VA researchers. This report sought to describe experiences and lessons learned from the first 3 years of a national research engagement service, featuring a Veteran Engagement (VE) Panel, established by the Consortium. We gathered authors' experiences to describe development and operation of the Consortium's VE Panel. Engagement staff collected program evaluation data about partners (Veterans and researchers), projects about which the VE Panel consulted, and meeting attendance during operation of the engagement service. We created a 12-member VE Panel; all of whom had lived experience with chronic pain, prescription opioid medication use, or opioid use disorder. Engagement staff and VE Panel members implemented an engagement service operational model designed to continuously learn and adapt. The panel consulted on 48 projects spanning the research process. Seventy-eight percent of panel members, on average, attended each monthly meeting. VE Panel members and participating researchers reported high satisfaction with the quality, ease, and outcomes of their engagement service experiences. This work provides an illustrative example of how a national research consortium facilitated Veteran-engaged research and built community between Veterans and VA researchers by developing and operating an ongoing engagement consulting service, featuring a VE Panel. The service, designed as a learning community, relied on skilled engagement staff to cultivate high quality experiences and outcomes for all partners.

Adaptive QoE‐based video delivery by optimizing the bitrate of the video course using network bandwidth and user bandwidth

SummaryWith the increase in internet connectivity worldwide, people have resorted to various video streaming applications for entertainment, education, healthcare, and so on. However, this has resulted in huge internet traffic, which is predominantly occurring due to the increased transmission of videos over wireless networks to mobile devices, Transmission of ultra‐high‐definition videos can be effortlessly carried out, although the video quality perceived by the users is normally lesser than the anticipated quality. In video streaming applications, it is a pre‐requisite to provide users with high quality of experience (QoE) but this is generally affected by the dynamic variations in the network conditions. This work presents a novel QoE‐based video delivery system in a collaborative e‐learning platform. Here, a novel deep learning structure recurrent neural network‐long short term memory (RNN‐LSTM) is developed for adaptive bit rate (ABR) selection, thereby providing users with videos of high QoE. Various network, time, and buffer‐based parameters are considered while selecting the bit rate. Additionally, the proposed RNN‐LSTM is assessed for its superiority in providing high QoE videos based on measures, like QoE, buffer size, and bit rate, and is observed to attain values of 0.975, 45.654, and 90.766 b/s, respectively.

An empirical assessment of the use of an algorithm factory for video delivery operations.

Video service providers are moving from focusing on Quality of Service (QoS) to Quality of Experience (QoE) in their video networks since the users' demand for high-quality video content is continually growing. By focusing on QoE, video service providers can provide their subscribers with a more personalized and engaging experience, which can help increase viewer satisfaction and retention. This focus shift requires not only a more sophisticated approach to network management and new tools and technologies to measure and optimize QoE in their networks but also a novel approach to video delivery operations. This paper describes the components, interactions, and relationships of an algorithm factory for video delivery operation that assures high QoE for video streaming services. The paper also showcases the results of gradually implementing an algorithm factory in the video industry. Using a dataset from 2016 to 2022, we present the case of a European PayTV service provider that achieved improved performance measured by both objective and subjective metrics. The use of an algorithm factory significantly improved the PayTV service provider's performance. The study found a fivefold increase in the speed of critical incident resolution and a 59% reduction in the number of critical incidents, all while expanding the customer base and maintaining the same level of labor resources. The case also demonstrates a strong positive relation between the productivity measures of the PayTV operator and their survey-based quality ratings. These results underscore the importance of flawless QoS and operational excellence in delivering QoE to meet the evolving demands of viewers. The paper adds to the existing literature on relationships between operational efficiency, innovation, and subjective quality. The paper further offers empirical evidence from the PayTV industry. The insights provided are expected to benefit both traditional and over-the-top (OTT) video service providers in their quest to stay ahead in the rapidly evolving video industry. It may also translate to other service providers in similar industries committed to supporting high-quality service delivery.

GreenABR+: Generalized Energy-Aware Adaptive Bitrate Streaming

Adaptive bitrate (ABR) algorithms play a critical role in video streaming by making optimal bitrate decisions in dynamically changing network conditions to provide a high quality of experience (QoE) for users. However, most existing ABRs suffer from limitations such as predefined rules and incorrect assumptions about streaming parameters. They often prioritize higher bitrates and ignore the corresponding energy footprint, resulting in increased energy consumption, especially for mobile device users. Additionally, most ABR algorithms do not consider perceived quality, leading to suboptimal user experience. This paper proposes a novel ABR scheme called GreenABR+, which utilizes deep reinforcement learning to optimize energy consumption during video streaming while maintaining high user QoE. Unlike existing rule-based ABR algorithms, GreenABR+ makes no assumptions about video settings or the streaming environment. GreenABR+ model works on different video representation sets and can adapt to dynamically changing conditions in a wide range of network scenarios. Our experiments demonstrate that GreenABR+ outperforms state-of-the-art ABR algorithms by saving up to 57% in streaming energy consumption and 57% in data consumption while providing up to 25% more perceptual QoE due to up to 87% less rebuffering time and near-zero capacity violations. The generalization and dynamic adaptability make GreenABR+ a flexible solution for energy-efficient ABR optimization.

Within-Year Teacher Turnover in Head Start and Children’s School Readiness

Teachers in early childhood education (ECE) settings are central to providing children with high-quality experiences that promote both early development and long-term well-being; unfortunately, rates of teacher turnover are high in ECE settings. There are strong theoretical reasons to assume turnover is negatively linked with children’s academic and socioemotional development, but few empirical studies test this hypothesis. Using an econometric fixed effects approach in two waves of data from the nationally representative Head Start Family and Child Experiences Survey, this study provides the first national estimate of the relationship between within-year lead teacher turnover and children’s development in Head Start. I find an annual within-year turnover rate of ~9%, about twice that of K–12, and that turnover is negatively associated with children’s language outcomes alongside suggestive evidence for behavioral outcomes.

MADRL-Based Joint Edge Caching and Bitrate Selection for Multicategory 360° Video Streaming

360-degree video streaming has gained increasing attraction in the current popular VR, AR, and MR applications, which can provide users with an immersive experience. In tile-based 360-degree video streaming, edge caching and bitrate selection strategies are jointly designed to improve users’ Quality of Experience (QoE), which incorporates video quality and rebuffer. However, the existing QoE-driven approaches use a unified QoE function to guide the decisions of edge caching and bitrate selection, which neglect the impact of video quality and rebuffer on different categories of 360-degree videos, thus failing to provide high average QoE for users. In this paper, we propose a MADRL-based joint edge caching and bitrate selection strategy for multicategory 360-degree video streaming to improve users’ average QoE. The key idea is to employ different edge caching and bitrate selection strategies for different video categories to enable fine-grained performance optimization. Based on multicategory 360-degree video streaming, we first model a joint edge caching and bitrate selection problem as a multi-agent cooperative Markov decision process with the goal of maximizing users’ average QoE. Next, an FoV-aware multi-agent soft actor-critic (FA-MASAC) algorithm is designed to help agents collaboratively learn optimal edge caching and bitrate selection decisions in a distributed way, in which each video category is treated as an agent. Finally, experimental results on real-world datasets show that our proposed strategy can greatly benefit users’ average QoE compared to existing strategies.

Mentor Teacher Perceptions of Effective University Supervisors: Prioritizing Collaboration and Community

Ensuring teacher candidates have high-quality experiences in their clinical placements is essential for sustaining their progress as teachers, and ultimately, the teaching profession. This article explores mentor teachers’ perceptions of effective university supervisors and the ways in which the mentors’ perceptions of effective supervision aligned with Burns’s et al. (2020) Framework for the Scope and Nature of Teacher Candidate Supervision. The authors interviewed 53 mentor teachers and analyzed data based on Burns et al.’s framework of 84 pedagogical routines of practice. Results indicate mentors highly valued pedagogical routines associated with Burns et al.’s Collaboration and Community category, pointing specifically to the importance of university supervisors making themselves available through quality communication and frequent school visits. Mentor teachers in this study also appreciated university supervisors who adopted a learner stance and provided mentor teachers with timely support.

“Is It Smart to Be Thrifty?”: How Advertisers Navigated Message Strategies During the Great Depression

ABSTRACTDespite the major historical significance of the Great Depression, little scholarly research exists that analyzes the state of newspaper advertising during the 1930s, particularly in the hard-hit Dust Bowl states. Using Taylor’s six-segment message strategy wheel, this research examined display advertising during one of the most difficult years in the most difficult places: 1934 in the Dust Bowl region of Nebraska, Colorado, Kansas, Oklahoma, New Mexico, and Texas. Results show that essential services, such as banks, utilities, and transportation, primarily used the informational/rational strategy to provide reassurance and empathy to consumers. However, nonessential industries, such as cosmetics, expensive home goods, and tobacco, often used the transformational/sensory approach to encourage high-quality experiences through spending. This historical analysis offers a unique opportunity for contemporary advertisers to understand how past advertising strategies during a crisis influence the present.

First and final farewells, disrupted family connections and loss: A collective case study exploring the impact of COVID-19 visitor restrictions in critical care

BackgroundPatient and family-centred care is considered best practice. Such an approach is associated with high quality and positive experiences of care, and family presence at the bedside is encouraged and enabled. The COVID-19 pandemic, however, resulted in strictly enforced restrictions on hospital visitation, which threatened health professionals’ ability to provide family-centred care. AimTo explore the impact of COVID-19 visitor restrictions on family relationships during critical illness at the end of life in the intensive care unit. DesignA retrospective collective case study approach was taken, using semi-structured interviews, conducted via telephone or Zoom, in accordance with COVID-19 restrictions. Setting/participantsTwo participant groups, bereaved next-of-kin of patients who died in the intensive care unit (n = 6) and critical care nurses (n = 3) from a major metropolitan hospital were included. FindingsInterviews with bereaved next-of-kin lasted 25–59 (mean = 41) minutes, and critical care nurse interviews lasted 31–52 (mean = 43) minutes. Inductive content analysis revealed five themes: (i) the first farewell, the significance not realised at the time; (ii) confusing rules and restrictions, which emphasised physical and created emotional barriers to family connections; (iii) inadequate communication, which further impacted next-of-kin; (iv) final farewells, which were rushed, emotional and afforded no privacy; and (v) reflecting back. ConclusionsThis collective case study demonstrates the profound impact visitor restrictions have had on bereaved next-of-kin and the wider family. A family-centred approach to care, protecting and prioritising family connection, and recognising the patient as a person who is part of a larger family unit must be emphasised. Implications for clinical practiceCritical care teams must consider their own approach to end-of-life care during times of visitor restrictions, finding new, flexible and innovative ways to improve communication, promote family-centred care, maintain the patient-family connection and facilitate end-of-life cultural customs, and rituals imperative to next-of-kin and the wider family unit.

Research on Orderly Charging Method of Electric Vehicles Based on Deep Reinforcement Learning

Electric vehicle charging stations are vital for the integration of electric vehicles (EV) with the future energy internet. To minimize daily operating costs in the face of uncertain time-of-use pricing and user behavior, this paper proposes an ordered charging optimization strategy from the perspective of load aggregators. By modeling the optimization problem as a finite-horizon Markov Decision Process (MDP) and using a reward-penalty feedback function to extract high-quality experience, the optimal operating strategy can be obtained. Results show that the proposed strategy reduces daily operating costs, protects batteries, and meets the charging.

Dyna-Validator: A Model-based Reinforcement Learning Method with Validated Simulated Experiences

Dyna is a planning paradigm that naturally weaves learning and planning together through environmental models. Dyna-style reinforcement learning improves the sample efficiency using the simulation experience generated by the environment model to update the value function. However, the existing Dyna-style planning methods are usually based on tabular methods, only suitable for tasks with low-dimensional and small-scale space. In addition, the quality of the simulation experience generated by the existing methods cannot be guaranteed, which significantly limits its application in tasks such as continuous control of high-dimensional robots and autonomous driving. To this end, we propose a model-based approach that controls planning through a validator. The validator filters high-quality experiences for policy learning and decides whether to stop planning. To deal with the exploration and exploitation dilemma in reinforcement learning, a combination of ϵ-greedy strategy and simulated annealing (SA) cooling schedule control is designed as an action selection strategy. The excellent performance of the proposed method is demonstrated in a set of classical Atari games. Experimental results show that learning dynamic models in some games can improve sample efficiency. This benefit is maximized by choosing the proper planning steps. In the optimization planning phase, our method maintains a smaller gap with the current state-of-the-art model-based reinforcement learning (MuZero). In order to achieve a good compromise between model accuracy and optimal programming step size, it is necessary to control the programming reasonably. The practical application of this method in a physical robot system helps reduce the influence of an imprecise depth prediction model on the task. Without human supervision, it is easier to collect training data and learn complex skills (such as grabbing and carrying items) while being more effective at scaling tasks that have never been seen before.

A novel health-aware deep reinforcement learning energy management for fuel cell bus incorporating offline high-quality experience

Data-driven intelligent energy management strategy (EMS) helps to further improve the performance and efficiency of fuel cell hybrid electric bus (FCHEB). However, most deep reinforcement learning (DRL) algorithms suffer from disadvantages such as overestimation and poor training stability, which limit the optimization effectiveness of the strategy. In addition, DRL-based EMSs tend to achieve good control only for the set optimization objectives and cannot be generalized to optimization objectives beyond the reward function. To solve the above problems, a novel health-aware DRL energy management for FCHEB is proposed in this paper. Firstly, based on the actual collected city bus driving cycles, a large amount of high-quality learning experience containing health-aware information is obtained through an advanced model predictive control strategy. Secondly, the state-of-the-art Twin Delayed Deep Deterministic policy gradient (TD3) algorithm is combined with offline high-quality learning experience to address the inherent shortcomings during the “cold-start phase” and to enhance the generalization capability of the proposed strategy. Finally, validation results showed that the proposed EMS improves training efficiency by 61.85% and fuel economy by 7.45%, extends fuel cell life by 4% and battery life by 19.4% compared to the conventional TD3-based EMS.