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The purpose of this paper is to provide meaningful information to sport organizations that have just recently started hosting sporting events in a new market and want to build long-term relationships with that market’s consumers.

Using self-administered questionnaires, the study surveys 463 spectators who attended the 2012 Formula One Korean Grand Prix.

Prior affect and existing sensory cues are crucial for spectators’ consumption behavior. Spectators’ prior event attendance plays a significant role on perceiving sensory stimuli as well as on consequent outcomes. This study also finds arousal significantly affected by seeing and hearing and prior affect. Arousal mediates the effects of these sensory stimuli and prior affect on satisfaction and revisit intention.

This study expands the knowledge on spectators’ experience at live sporting events. In addition, more dimensions of the mechanisms that are at work between cognitive elements and sensory stimuli are added to the existing knowledge of sport spectators’ behavior.

The purpose of this study was to identify the relationship between customer values, satisfaction and loyalty in the context of Airbnb.

The survey questionnaires were developed based upon review of previous studies. The samples were collected from US- based Airbnb users. The data were analyzed using structural equation modeling.

The result of this study indicates that first, Airbnb users’ hedonic value has a positive impact on satisfaction and loyalty, while utilitarian value influences only on satisfaction. Second, this study also shows that product involvement plays as a moderating role in the paths between hedonic value and customer satisfaction.

Study findings may help researchers understand the roles of hedonic and utilitarian values and their impacts on satisfaction and loyalty in the context of Airbnb. This study also contributes to Airbnb marketers in fulfilling users’ expectations about the company.

This research contributes to the hospitality literature by expanding the realm of research on Airbnb, which is a unique company applying the sharing economy concept in the lodging industry.

This study aims to examine the brand tourism effect observed in luxury hotels. The study assumed that when loyal customers of luxury hotels perceive two different types of non-loyal customers, loyal customers’ perceptions might influence their behavioral intention. In addition, two emotions (i.e. anger and pride) might mediate the relationship between perceptions and behavioral intention.

This study used a scenario-based experimental design. Data from 1,013 responses were analyzed using partial least squares-structural equation modeling.

The study reveals that when loyal customers view brand tourists, infringement has a positive influence on switching intention, and likability positively effects brand loyalty. This study indicated that loyal customers perceive brand immigrants negatively, whereas brand tourists are comparably positively perceived by loyal customers. Only pride mediates these two relationships, respectively.

The study confirmed the brand tourism effect in luxury hotels by indicating a clearer relation between perception, emotion and behavioral intention. The theoretical implications could suggest insightful guidelines for future studies regarding loyalty in luxury hotels.

The purpose of this study is to reveal the research trends in guest experiences of service robots in the hospitality industry.

In this study, a review was carried out on the Web of Science (WoS) database with the assistance of bibliometric analysis techniques. Cluster analysis was also employed for this to group important data to determine the relationships and to visualize the areas in which the studies are concentrated. The thematic content analysis method was used to reveal on which customer experiences and on which methods the focuses were.

On the subject of experiences of service robots, the greatest number of publications was in 2021. In terms of country, China has come to the fore in the distribution of publications. As a result of thematic content analysis, it was determined that the leading factor was the main dimension of emotional experience. In terms of sub-dimensions, social interactions attracted more attention. Most of the studies discussed were not based on any theory. Apart from these, the Technology Acceptance Model (TAM), the Service Quality Model (SERVQUAL) and Perceived Value Theory (PVT) were featured more prominently among other studies.

In this study, only the WoS database was reviewed. In future studies, it would be possible to make contextual comparisons by scanning other databases. In addition to quantitative research designs, social dimensions may be examined in depth following qualitative research methods. Thus, various comparisons can be made on the subject with mixed-method research designs. Experimental research designs can also be applied to where customers have experienced human-robot interactions (HRIs).

In the hospitality industry, it is critical to uncover every dimension of guests' robot acceptance. This study, which presents the current situation on this basis, guides future projections for the development of guest experiences regarding service robots in the hospitality industry.

This study explores how recent Korean immigrant students experience learning social studies and how their unique social, cultural, and educational backgrounds as new immigrants shape their experiences in American schools. Based on survey and in-depth interviews with 43 Korean immigrant students in two urban and three suburban/rural areas, this mixed methods study examines Korean immigrant youths’ perceptions about the nature of history and social studies as well as their experiences of learning social studies in their everyday classroom contexts. Our data analysis demonstrates that Korean immigrant students face varying difficulties in constructing meaning in US history and engaging themselves in social studies learning, which results in a negative learning experience and subsequent disinterest in social studies. Researchers identified three major challenges that Korean immigrant youths experience in their social studies classrooms: (1) Lack of English proficiency, background knowledge, and American patriotism, (2) White, American-centered perspectives and marginalization of their country of origin, and (3) Teachers’ lack of care and disengaging pedagogies. The findings of this study provide implications for creating more meaningful and culturally relevant social studies learning for immigrant students.

This work highlights the potential of blockchain from a public management (PM) angle. Given the limited presence of blockchain's broad-spectrum, empirical-applicative evidence in the public sector, as well as theoretical systematisations that can exemplify its potential within this scope, the authors have concentrated their efforts on structuring a referring model.

After identifying a gap and conducting a preliminary literature review related to public administration (PA), the authors propose a paradigm focused on a conceptual synthesis methodology, starting from a longitudinal analysis and a coding activity that are able to structure a clear framework of theoretical parallelism regarding blockchain's main functionalities, as well as future perspectives in the public sector.

The main results yielded a specific longitudinal literature review and constitute a referring model of blockchain's systematised functionalities through a conceptualised matrix. The outcome of the conceptualisation process frames and systematises a rapidly growing controversial phenomenon, furnishing a specific referring paradigm for the issue as it relates to policymakers.

The study's originality resides in the two views the authors created, both from the literature review and from the conceptual synthesis for public sector operative practice via an anthropocentric lens, conveyed by a three-year range of analysis.

When wearable and implantable devices first arose in the 1970s, they were rigid and clashed dramatically with our soft, pliable skin and organs. The past two decades have witnessed a major upheaval in these devices. Traditional electronics are six orders of magnitude stiffer than soft tissue. As a result, when rigid electronics are integrated with the human body, severe challenges in both mechanical and geometrical form mismatch occur. This mismatch creates an uneven contact at the interface of soft-tissue, leading to noisy and unreliable data gathering of the body’s vital signs. This paper aims to predict the role that discreet, seamless medical devices will play in personalized health care by discussing novel solutions for alleviating this interface mismatch and exploring the challenges in developing and commercializing such devices.

Since the form factors of biology cannot be changed to match those of rigid devices, conformable devices that mimic the shape and mechanical properties of soft body tissue must be designed and fabricated. These conformable devices play the role of imperceptible medical interfaces. Such interfaces can help scientists and medical practitioners to gain further insights into the body by providing an accurate and reliable instrument that can conform closely to the target areas of interest for continuous, long-term monitoring of the human body, while improving user experience.

The authors have highlighted current attempts of mechanically adaptive devices for health care, and the authors forecast key aspects for the future of these conformable biomedical devices and the ways in which these devices will revolutionize how health care is administered or obtained.

The authors conclude this paper with the perspective on the challenges of implementing this technology for practical use, including device packaging, environmental life cycle, data privacy, industry partnership and collaboration.

The research aims at finding out the extent to which such urban transformation projects—in terms of their design components and aspects—contribute to providing big data (BD) with effective datasets, which are considered a key requirement in operating smart city solutions.

The present research investigates the predisposition level of these projects for supporting the BD through one of the urban initiatives for humanizing neighborhoods in Al Riyadh city (KSA). The selected case is analyzed by mapping the functional performance of its design features with certain characteristics of BD.

One of the core conclusions is: Despite the readiness of the design components of these projects to provide high levels of integration with IoT and smart transformation, the level of application is lower than expected on account of the dearth of initial targets and strategies, especially the lack of a supporting environment, street facilities and security and safety datasets.

The answer—arrived at through both descriptive and analytical approaches—is expected to guide those who query the further key concept of smart applications—is the answer characterized as technologically based, or is the same based on human aspects?

The purpose of this paper is to explore the possibility of integrating inkjet printed circuitry with fused deposition modeling (FDM) structures to produce embedded electronics and smart structures. Several of the challenges of combining these technologies are identified, and potential solutions are developed.

An experimental approach is taken to investigate some of the relevant physical processes for integrating FDM and inkjet deposition, including the printing, drying and sintering processes. Experimental data are collected to assist understanding of the problems, and engineering solutions are proposed and implemented based on the gained understanding of the problems.

Three challenges have been identified, including the discontinuity of the printed lines resulting from the irregular surface of the FDM substrate, the non-conductivity of the printed lines due to the particle segregation during the droplet drying process and the slow drying process caused by the “skinning effect”. Two engineering solutions are developed for the discontinuity problem. The non-conductivity issue and the slow drying process are attributed to the motion of the nanoparticles caused by the evaporation flow. The thermally activated drying process for the Cabot ink suggests that the proposed solution is effective. Timescale analysis and experimental data show that the printing conditions do not have a clear influence on the conductivity of the printed lines, and drying and sintering processes are more important.

No quantitative model has yet been developed for simulating the printing, drying and sintering processes associated with inkjet printing on FDM substrates. Quantitative models can be extremely valuable for improvement in understanding the problems, optimizing the proposed solutions and coming up with better solutions.

The research findings in this work have great implications in implementing a hybrid FDM-inkjet deposition machine for fabricating embedded electronics and smart structures. All the proposed engineering solutions for the identified problems can be potentially integrated into one machine.

The success of the integration of the FDM and inkjet deposition process will enable the design of compact electro-mechanical structures to replace the large heavy electro-mechanical systems.

This work represents one of the first attempts for integrating inkjet deposition of silver nanoparticle inks with the FDM process for making compact electro-mechanical structures. Three critical challenges are identified, and corresponding engineering solutions are proposed and implemented based on analysis of the relevant physical processes, including the printing, drying and sintering processes, which has laid the foundation for integrating the FDM and inkjet deposition processes.

Because the dynamic characteristics of the stock market are nonlinear, it is unclear whether stock prices can be predicted. This paper aims to explore the predictability of the stock price index from a long-memory perspective. The authors propose hybrid models to predict the next-day closing price index and explore the policy effects behind stock prices. The paper aims to discuss the aforementioned ideas.

The authors found a long memory in the stock price index series using modified R/S and GPH tests, and propose an improved bi-directional gated recurrent units (BiGRU) hybrid network framework to predict the next-day stock price index. The proposed framework integrates (1) A de-noising module—Singular Spectrum Analysis (SSA) algorithm, (2) a predictive module—BiGRU model, and (3) an optimization module—Grid Search Cross-validation (GSCV) algorithm.

Three critical findings are long memory, fit effectiveness and model optimization. There is long memory (predictability) in the stock price index series. The proposed framework yields predictions of optimum fit. Data de-noising and parameter optimization can improve the model fit.

The empirical data are obtained from the financial data of listed companies in the Wind Financial Terminal. The model can accurately predict stock price index series, guide investors to make reasonable investment decisions, and provide a basis for establishing individual industry stock investment strategies.

If the index series in the stock market exhibits long-memory characteristics, the policy implication is that fractal markets, even in the nonlinear case, allow for a corresponding distribution pattern in the value of portfolio assets. The risk of stock price volatility in various sectors has expanded due to the effects of the COVID-19 pandemic and the R-U conflict on the stock market. Predicting future trends by forecasting stock prices is critical for minimizing financial risk. The ability to mitigate the epidemic’s impact and stop losses promptly is relevant to market regulators, companies and other relevant stakeholders.

Although long memory exists, the stock price index series can be predicted. However, price fluctuations are unstable and chaotic, and traditional mathematical and statistical methods cannot provide precise predictions. The network framework proposed in this paper has robust horizontal connections between units, strong memory capability and stronger generalization ability than traditional network structures. The authors demonstrate significant performance improvements of SSA-BiGRU-GSCV over comparison models on Chinese stocks.