Entertainment Domains Research Articles

Metaphorical framing of political events through ENTERTAINMENT scenarios

Abstract The article sets out to examine the entertainment source domain and its scenarios as realised in the media discourse surrounding political events in Belarus in 2021. Metaphorical scenarios are investigated considering different viewpoints, such as those presented in Lithuanian, German, and Russian media. The methodological framework is a blend of Cognitive Linguistics and Critical Discourse Studies, embracing the viewpoint (Sweetser, 2012), Critical Metaphor Analysis (Charteris-Black, 2005), metaphorical scenarios (Musolff, 2016), and framing (Hart, 2014). The results demonstrate that the entertainment domain acquires multiple scenarios depending on the viewpoint. The evaluation attached to the scenarios is mostly negative; however, the viewpoint adopted in Lithuanian and German discourses is very different from Russian discourse. The former is based on Western values and targets the authoritarian regime, whereas the latter is Russian-centric, with the Western world posited as a major threat to the Belarusian and Russian societies.

A structural model of pre-service teachers’ attitude, acceptance, and continuance intention towards mobile augmented reality

The increasing prevalence of mobile augmented reality (MAR) has garnered attention in contemporary times. However, its application within educational settings remains largely untapped, primarily due to its predominant utilization in entertainment domains. MAR presents opportunities for augmenting learning experiences, fostering enhanced adaptability in educational methodologies, and enriching students’ academic endeavors. Therefore, this study aims to enhance the use of MAR and formulates a model of MAR usage for pre-service teachers. In the context of this study, MAR learning cardiovascular (MARLCardio) serves as an experimental tool for exemplifying the features of MAR. This study integrates the Unified Theory of Acceptance and Use of Technology (UTAUT), Technology Acceptance Model (TAM), and Expectation Confirmation Model (ECM) as underpinning theories. This study was undertaken to determine factors that influence pre-service teachers’ attitude, acceptance, and their intention to continue using MAR in the learning process. Responses from 456 pre-service teachers through a survey were analyzed using structural equation modelling. Findings in this study found that pre-service teachers agree that facilitating condition play a crucial role in shaping pre-service teachers’ positive attitude and influencing their intention to continue using MAR. Meanwhile, performance expectancy plays a major role in ensuring pre-service teachers’ acceptance of using MAR in their learning process. It was anticipated that the formulated MAR structural model would offer insights and support forthcoming research endeavors aimed at fostering the sustainability of MAR utilization.

Towards Emotionally Intelligent Virtual Environments: Classifying Emotions through a Biosignal-Based Approach

This paper introduces a novel method for emotion classification within virtual reality (VR) environments, which integrates biosignal processing with advanced machine learning techniques. It focuses on the processing and analysis of electrocardiography (ECG) and galvanic skin response (GSR) signals, which are established indicators of emotional states. To develop a predictive model for emotion classification, we extracted key features, i.e., heart rate variability (HRV), morphological characteristics, and Hjorth parameters. We refined the dataset using a feature selection process based on statistical techniques to optimize it for machine learning applications. The model achieved an accuracy of 97.78% in classifying emotional states, demonstrating that by accurately identifying and responding to user emotions in real time, VR systems can become more immersive, personalized, and emotionally resonant. Ultimately, the potential applications of this method are extensive, spanning various fields. Emotion recognition in education would allow further implementation of adapted learning environments through responding to the current emotional states of students, thereby fostering improved engagement and learning outcomes. The capability for emotion recognition could be used by virtual systems in psychotherapy to provide more personalized and effective therapy through dynamic adjustments of the therapeutic content. Similarly, in the entertainment domain, this approach could be extended to provide the user with a choice regarding emotional preferences for experiences. These applications highlight the revolutionary potential of emotion recognition technology in improving the human-centric nature of digital experiences.

Content-based Recommender System Using Cosine Similarity

Abstract: In the quest for a more tailored cinematic experience, this paper presents a Movie Recommender System that harnesses the power of Cosine Similarity within a machine learning framework. The system’s cornerstone is its ability to discern nuanced user preferences and suggest films that resonate on a personal level. Employing Python, the research delineates a methodology that encompasses data collection, preprocessing, and feature extraction from a comprehensive dataset of movies. The crux of the system lies in its application of the cosine similarity algorithm, which calculates the affinity between movies and users based on shared characteristics such as genre, director, plot, title and cast. This approach is rigorously evaluated using metrics like precision, recall, and accuracy, ensuring the recommender’s reliability. The experimental design bifurcates the dataset into training and testing subsets, fortifying the system’s robustness. The paper’s findings illuminate the system’s efficacy in delivering precise and individualized recommendations, thereby augmenting the user’s movie selection process. It underscores the potential of the system to revolutionize content discovery and consumption in the entertainment domain. The conclusion encapsulates the project’s ambition to refine the recommender system further by integrating diverse machine learning algorithms and scaling its capabilities.

The Difference of Consumption Between Urban and Rural Areas in Jiangsu

This research delves into the intricate urban-rural consumption disparities within Jiangsu Province, China, over the decade spanning from 2011 to 2021. Specifically, it explores disparities in the domains of food, health care, culture, and entertainment, shedding light on the multifaceted dimensions of inequality in these critical aspects of daily life. The study employs a comprehensive approach, combining quantitative data analysis and qualitative insights, to provide a holistic understanding of the socio-economic factors driving these disparities. The findings reveal a stark divergence in consumption patterns between urban and rural areas of Jiangsu Province. In the realm of food, urban residents have access to a wider variety of choices and higher-quality options compared to their rural counterparts. Disparities in healthcare access and utilization are also evident, with urban areas benefitting from more advanced medical facilities and services. Furthermore, cultural and entertainment opportunities are concentrated in urban centers, leaving rural residents with limited access to enriching experiences. This research is of paramount importance as it underscores the pressing need for policies and interventions aimed at bridging the urban-rural consumption gap in Jiangsu Province. The study's findings have significant implications for regional development strategies, social equality initiatives, and the overall well-being of Jiangsu residents. Understanding the intricate dynamics of consumption disparities is crucial for fostering a more equitable and sustainable future for all residents of the province.

The Research on the Transformations in "Pleasant Goat and Big Big Wolf" and Attitudes Towards the Animation Industry in China

China experienced a period of rapid development on entertainment domain. While some animations became extremely famous in a short time but quickly disappeared at the market, others could keep on track with the alteration of society. In this paper, the researchers will focus on an animation called Pleasant Goat and Big Big Wolf and alterations of the culture and society in China behind its changes. The authors discuss reasons leading to changes of the animation and estimate alterations happened on society through researching developments of Chinese animation industry during last 15 years and making comparisons in its characters, plots, and painting styles between different editions of the animation published in last 15 years. In this paper, people could find out some reasons about changes of this animations in last 15 years and changes of peoples attitudes toward animation industry on society.

If it is bad, why don’t I quit? Algorithmic recommendation use strategy from folk theories

Users persist in utilizing algorithmic recommendations despite perceiving their adverse consequences, such as privacy invasion and filter bubbles. This behavior appears contradictory to the innate human inclination to seek benefits and avert disadvantages. Employing folk theories, this study conducted in-depth interviews with 24 users who acknowledged the detrimental effects of algorithms and maintained a non-positive attitude toward them but continued their usage. The findings revealed that users adhere to the principle of rational choice: they engage in positive risk aversion (confining their usage to entertainment domain and affirming their own abilities) and negative self-avoidance (acknowledging the inevitability of privacy invasion, imagining the additional costs of turning off algorithmic recommendations, and believing that everyone relies on algorithms, rendering the costs relatively inconspicuous). Throughout this process of self-adaptation, users assumed diverse roles (the inactive, active, resister, swayer) to achieve a harmonious state and sustain their reliance on algorithmic recommendations.

The Efficacy and Utility of Lower-Dimensional Riemannian Geometry for EEG-Based Emotion Classification

Electroencephalography (EEG) signals have diverse applications in brain-computer interfaces (BCIs), neurological condition diagnoses, and emotion recognition across healthcare, education, and entertainment domains. This paper presents a robust method that leverages Riemannian geometry to enhance the accuracy of EEG-based emotion classification. The proposed approach involves adaptive feature extraction using principal component analysis (PCA) in the Euclidean space to capture relevant signal characteristics and improve classification performance. Covariance matrices are derived from the extracted features and projected onto the Riemannian manifold. Emotion classification is performed using the minimum distance to Riemannian mean (MDRM) classifier. The effectiveness of the method was evaluated through experiments on four datasets, DEAP, DREAMER, MAHNOB, and SEED, demonstrating its generalizability and consistent accuracy improvement across different scenarios. The classification accuracy and robustness were compared with several state-of-the-art classification methods, which supports the validity and efficacy of using Riemannian geometry for enhancing the accuracy of EEG-based emotion classification.

Fans Optimizer: A human-inspired optimizer for mechanical design problems optimization

This paper proposes a human-inspired algorithm for optimizing various practical engineering problems, specifically, the Fans Optimization (FO) algorithm that is inspired by the Fans economy fused in the entertainment domain. Opposing current algorithms, the FO algorithm introduces a Multi-groups mechanism (Cooperation–Competition) and a Two-characteristic individual update mechanism to balance the exploration and exploitation (E&E). Additionally, the multi-phase optimization algorithm includes Roles information and Fans-community, Fans-community Switching, Resource Competition, Information Sharing, and K-Update. In the experiments, the FO algorithm is first compared with 12 meta-heuristic algorithms in four groups of benchmark functions (selected from the IEEE CEC and GECCO competitions), demonstrating that the FO algorithm has a superior convergence performance and E&E. Moreover, the Williams test prove the superiority of the FO algorithm over the competitor algorithms. Additionally, eight class practical engineering problems verify the FO’s practical engineering applicability. Finally, the FO algorithm solves the inverse kinematic solution problem of the 9-degree of freedom serial robot arm (9-DFSRA) , demonstrating superior results over the competitor schemes. Overall, the test results prove that the FO algorithm is superior to its competitor algorithms for complex engineering problems.

Why Do Candidates Forgo Cochlear Implantation?

Identify barriers and facilitating factors in cochlear implant (CI) utilization by comparing functional measures between CI candidates who undergo or forgo implantation. Forty-three participants were separated into two groups: (1) 28 participants who underwent CI and (2) 15 participants who elected not to proceed with CI despite meeting eligibility criteria (no-CI). Prior to implantation, all participants completed the CI Quality of Life (CIQOL)-35 Profile and CIQOL-Expectations instrument. They were also surveyed on factors contributing to their decision to either undergo or forgo CI. Word and speech recognition were determined using the Consonant-Nucleus-Consonant (CNC) and the AzBio tests, respectively. CIQOL-Expectations scores were indistinguishable between groups, but there were substantial differences in baseline CIQOL-35 Profile scores. Compared to the CI group, the no-CI group exhibited higher pre-CI scores in the Emotional (Cohen's d [95% CI] = 0.8 [0.1, 1.5]) and Entertainment (Cohen's d [95% CI] = 0.8 [0.1, 1.5]) domains. Survey data revealed that the most commonly reported barriers to pursuing CI in the no-CI cohort were fear of surgical complications (85%), cost associated with implantation (85%), and perception that hearing was not poor enough for CI surgery (85%). The results of this study indicate that functional outcome expectations are similar between candidates who elect to receive or forgo CI, yet those who forgo CI have higher baseline CI-specific QOL abilities. 4 Laryngoscope, 133:3548-3553, 2023.

Political Influencers on Social Media: An Introduction

Influencers are omnipresent on social media platforms. They occupy important digital real estate across a range of topical domains including beauty, fashion, and gaming. While researchers have contributed important work on the respective role that authenticity plays for influencers’ success and have described a burgeoning industry within the larger domain of social media entertainment, comparably little is known about what happens when influencers get involved in politics, when they harness their digital clout to promote political causes and social issues, and thereby become political influencers. This introduction to the special issue on political influencers provides a definition of what makes someone a political influencer. It theoretically locates political influencers within the larger field of media and communication scholarship, and delineates the term from other, related concepts such as that of opinion leader. Building on eight contributions focused across more than six countries and nine platforms, the article showcases important strands of current and future research. It provides the foundation for a more systemic understanding of political influencers on social media, situating them within a media ecology complicated by a diverse array of traditional and nontraditional actors, tactics, and dynamics. The article concludes with recommendations for future research.

Consumers’ Preferences for Ethical Entertainment Consumption: Conceptualization, Development, and Validation of a Scale

Our study describes the conceptualization, development, and validation of a reliable scale to measure the Consumers’ Preferences for Ethical Entertainment Consumption (CPEEC) in the context of OTT platforms. Past literature has addressed Ethical Consumption in various contexts that hold significant importance among consumers. However, there is limited literature on the Ethical Consumption in the domain of OTT entertainment, which is a new and burgeoning field of literature. Conceptualizing and developing a measurement scale for CPEEC will help measure the ethicality of entertainment like films, web shows and reality shows. The CPEEC scale is developed and validated by performing 33 in-depth interviews, focus group discussions with 20 participants, and three online surveys with 707 respondents. Our findings highlight that the CPEEC construct is a formative second-order construct that consists of three first-order dimensions. Entertainment executives can use the CPEEC scale to strategize their existing and new content catalogs according to consumer values and beliefs.

Target Detection and Network Optimization: Deep Learning in Face Expression Feature Recognition

Facial expression recognition technology has been more and more in demand in security, entertainment, education, medical, and other domains as artificial intelligence has advanced, and face expression recognition technology based on deep learning has become one of the research hotspots. However, there are still some issues with the existing deep learning convolutional neural network; the feature extraction technique has to be improved, and the design of the detailed network structure needs to be optimized. It is critical to do more research on the deep learning convolutional neural network model in order to increase the accuracy of face facial expression detection. In this paper, a deep learning convolutional neural network structure combining VGG16 convolutional neural network and long and short-term memory networks is designed to address the shortcomings of existing deep learning methods in face expression recognition, which are prone to overfitting and gradient disappearance, resulting in low test accuracy. This structure easily and effectively collects facial expression information and then classifies the retrieved features using a support vector machine to detect face expressions. Finally, the fer2013 dataset is used to train face expression recognition, and the results demonstrate that the built deep convolutional neural network model can effectively increase face expression identification accuracy.

Working with animals: Implications for employees’ compassion, awe, prosocial behavior, and task performance

AbstractHuman‐animal work represents a collaboration between humans and animals to achieve work goals, and is common in the domains of healthcare, therapy, entertainment, and education. Although the scope and types of human‐animal work is diversifying and increasing, organizational scholars have yet to explore its impacts on employees. Drawing from the models of compassion and mind perception theories, we first develop a theoretical model pertaining to the development of compassion as a result of human‐animal work. In a study with zookeepers (Study 1), we find that human‐animal work evokes the emotion of compassion, which in turn is positively associated with employee prosocial behavior and task performance. These mediated effects are moderated by how employees perceive animals—employees are more likely to experience compassion, and in turn, become more prosocial and work better when they generally perceive animals to be able to experience emotions and bodily sensations. Furthermore, two follow‐up studies (i.e., Studies 2 and 3) with employees who engage in human‐animal work in Hong Kong and the United States reveal that working with animals evokes awe in addition to compassion, and provides insight into their resultant impact on prosocial behavior and task performance. We end by discussing the theoretical and practical implications of this work.

Analysis of Intermediary Roles and Technical Components for Smartglass-Assisted Interactive Remote Collaboration

Recent advances in Internet-of-Things and wearable computing have established computationally sufficient infrastructure to remove the barriers of physical locations. The recent COVID-19 pandemic has accelerated the use of remote collaboration, work-from-home, teleconferencing, online education, digital twin, and metaverse enriched with various configurations of augmented reality, virtual reality, mixed reality, and extended reality. Beyond specialized niche wearable applications in the medical and entertainment domains, we aim to identify the underlying characteristics of smartglass-enabled killer applications for everyday use. In this paper, we analyze intermediary roles in remote collaboration consisting of authoritative supervisors, synergetic collaborators, and speculative explorers. We also propose technical components for smartglass-assisted interactive remote collaboration composed of personal tracking, teleconference, and interaction components. More specifically, the application tasks of each intermediary role and objectives of three functional components are defined and elaborated.

Mahem Entertainment Society: strategic alternatives and change management

Learning outcomes Teaching objectives: to help students review entertainment service and its ecosystem; to discuss leadership anxiety during change management and organizational development; to understand organizational diagnosis while initiating change management exercise; and to compare various strategic alternatives and the implications of selecting an option. Case overview/synopsis This case narrates dilemma of Krishna Goenkar, a management consultant entrusted to revisit strategic orientations of Mahem Entertainment Society (MES). Mahem is a fictitious state in the west coast of India. MES had been created by the Government of Mahem as a regulatory body to promote the state as a world class destination for entertainment. Public interest guided the organization, as it was a government instituted body. Hence, Goenkar had twin challenges. Firstly, what strategic initiatives should he propose to scale up the operations in spite of the given organizational constraints? Secondly, how to scale up and diversify if required, with minimal resistance? The case would help students get familiarized with entertainment domain, service ecosystem and challenges of driving strategic change in public utilities, especially in Indian and emerging market context. Complexity academic level This case is suitable for graduate-level programme in marketing management. Supplementary materials Teaching notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes. Subject code CSS 8: Marketing.

Eliciting Auxiliary Information for Cold Start User Recommendation: A Survey

Recommender systems suggest items of interest to users based on their preferences. These preferences are typically generated from user ratings of the items. If there are no ratings for a certain user or item, it is said that there is a cold start problem, which leads to unreliable recommendations. Existing studies that reviewed and examined cold start in recommender systems have not explained the process of deriving and obtaining the auxiliary information needed for cold start recommendation. This study surveys the existing literature in order to explain the various approaches and techniques employed by researchers and the challenges associated with deriving and obtaining the auxiliary information necessary for cold start recommendation. Results show that auxiliary information for cold start recommendation is obtained by adapting traditional filtering and matrix factorization algorithms typically with machine learning algorithms to build learning prediction models. The understanding of similar or connected user profiles can be used as auxiliary information for building cold start user profile to enable similar recommendations in social networks. Similar users are clustered into sub-groups so that a cold start user could be allocated and inferred to a sub-group having similar profiles for recommendations. The key challenges of the process for obtaining the auxiliary information involve: (1) two separate recommendation processes of conversion from pure cold start to warm start before eliciting the auxiliary information; (2) the obtained implicit auxiliary information is usually ranked and sieved in order to select the top rated and reliable auxiliary information for the recommendation. This study also found that cold start user recommendation has frequently been researched in the entertainment domain, typically using music and movie data, while little research has been carried out in educational institutions and academia, or with cold start for mobile applications.

Little Computer People

Bolstered by a growing interest in simulating believable non-player characters (NPCs), work on NPC models has spanned topics such as planning, procedural storytelling, decision-making, and social dynamics. However, research groups work in isolation, designing and discussing their character models with disparate approaches, often using project-specific terminology. This makes it challenging to identify, classify, and accumulate existing knowledge. It is our position that since modelling of virtual characters has become an integral part of the scientific practice in our field, we must develop a common taxonomy to discuss these models. With this goal in mind, we conduct an in-depth analysis of a selection of projects, categorizing existing agent social interactions, and comparing results from research-based and commercial social simulation works in the entertainment domain. We conceptualize a taxonomy that classifies agent interactions by their social behaviours, inter-agent communication, knowledge flow, and the change in their relationships. We posit such a taxonomy would allow scientists to reproduce and evaluate existing models, collaborate on standards, share advances with other researchers and practitioners, allow for better communication and methodologies developed for new techniques, and allow for a more rigorous model-to-model analysis.

The Effects of a Self-Similar Avatar Voice in Educational Games

Avatar identification is one of the most promising research areas in games user research. Greater identification with one's avatar has been associated with improved outcomes in the domains of health, entertainment, and education. However, existing studies have focused almost exclusively on the visual appearance of avatars. Yet audio is known to influence immersion/presence, performance, and physiological responses. We perform one of the first studies to date on avatar self-similar audio. We conducted a 2 x 3 (similar/dissimilar x modulation upwards/downwards/none) study in a Java programming game. We find that voice similarity leads to a significant increase in performance, time spent, similarity identification, competence, relatedness, and immersion. Similarity identification acts as a significant mediator variable between voice similarity and all measured outcomes. Our study demonstrates the importance of avatar audio and has implications for avatar design more generally across digital applications.

Product meanings and consumer behavior in sport entertainment: the case of World Wrestling Entertainment (WWE)

ABSTRACT Research question This research examines consumer perceptions about the official merchandise of World Wrestling Entertainment (WWE), a leader in global sports entertainment. The goals of the study were to extend the Meanings of Sport Licensed Products (MSLP) scale (Papadimitriou & Apostolopoulou, 2018) to the sport entertainment domain and test the reliability and validity of the adapted scale and to propose a segmentation scheme of WWE consumers based on the meanings they attribute to the WWE products they own. Research methods The sample included United States-based shoppers of the online WWE store. A total of 1211 surveys were collected through an online questionnaire that captured information about consumers’ demographic profile and WWE identification levels, the official WWE merchandise they owned and post-purchase satisfaction, product meanings attributed to WWE items, and behavioral intentions. Data analysis included Exploratory and Confirmatory Factor Analysis as well as regression analysis. Results and findings The result of this research is the Meanings of Sport Entertainment Licensed Products (MSeLP) scale, a 21-item reliable and valid instrument comprised of four dimensions of product meaning: identification and connectedness, experience, esthetics and personal history. This study further explores relationships between product meanings and four outcome variables (identification with WWE, number of items owned, word-of-mouth and future buying behavior) and proposes three distinct clusters of sport entertainment consumers based on the meanings they attribute to their possessions (‘rationals,’ ‘vested’ and ‘enthusiastic’). Implications Those involved in the development, positioning and sale of licensed products in the sport entertainment domain would benefit from an understanding of the meanings that consumers attach to their possessions, especially meanings related to identity construction.