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Human Behavior and Emerging Technologies is an interdisciplinary journal publishing high-impact research that advances the understanding of complex interactions between diverse human behavior and emerging digital technologies.
Editor spotlight
Chief Editor, Zheng Yan, is an Associate Professor and Division Director of educational psychology and methodology at University at Albany. His research focuses on the dynamic and complex relations between emerging technologies and human development.
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Latest Articles
More articlesAcademic Emotion Classification Using FER: A Systematic Review
Facial emotion expressions are among the most potent, natural, and powerful means of human communication. Due to the COVID-19 pandemic, educational institutions worldwide are forced to switch rapidly to remote and online learning. Students are currently in an emergency state and must adapt to various and readily accessible learning methods, such as mobile learning applications or an e-learning system. A systematic literature review (SLR) is conducted to extract and synthesize information such as the emotion classifier used in the facial expression recognition (FER) system, the dataset used, the preprocessing technique applied, the feature extraction approach used, and the strength and limitation of the previous studies. Based on the search criteria, 701 publications were initially retrieved from five different digital databases, of which 48 studies have been chosen as primary studies for further analysis. Based on the findings of this study, the deep learning approach is the most frequently adopted approach in classifying student emotions during online learning. FER-2013 is the most commonly used FER dataset in FER studies, while DAiSEE is the most used academic emotion dataset. Moreover, support vector machine (SVM) is the conventional learning emotion classifier that is widely used in the FER systems, while convolutional neural network (CNN) is the most frequently used deep learning classifier. Next, it was found that the number of real-time FER systems is less than that of non-real-time FER systems. Finally, the top-1 accuracy of 94.6% was achieved by the long-term recurrent convolutional network on the academic emotion dataset, and the limitation is that it has low illumination and a lack of frontal pose.
The Role of News Post Consumption on Facebook in Shaping Youth Perceptions of Safety and Civil Liberties during COVID-19 in the U.S., Spain, and Egypt
News media coverage, regardless of the platform being used, can shape people’s opinions, perceptions, behaviors, and preferences about policy, security, and civil liberties in times of crisis. This study examines the possible cultivation impact of news post exposure on Facebook on shaping threat perception. Furthermore, it aims at exploring the correlation between Facebook news exposure, preferring high security levels, and curbing some civil liberties during the COVID-19 pandemic. For this, we used the survey method () with youths aged 18-35 in the U.S., Spain, and Egypt. Our results showed that heavy news exposure on Facebook cultivated fear and terror perceptions during the COVID-19 pandemic. Moreover, it shaped people’s preference for a tightened security environment and willingness to trade off some civil liberties. In other words, despite the different media and political systems, heavy news post consumption on Facebook can increase the tendency to give up civil liberty rights and prefer a more stringent security environment.
A Study on the Improvement Direction of Artificial Intelligence Speakers Applying DeLone and McLean’s Information System Success Model
Recently, many artificial intelligence speakers have been released on the market worldwide. However, the AI speaker thinks autonomously, which is a characteristic of AI. There seems to be no element to talk about. Even answering the question of this study and saying, “It’s not like an artificial intelligence speaker, it’s like a slightly advanced Internet-connected radio.” To determine the difference between these released technologies and users’ expectations in detail, we tried to analyze whether the quality and value of artificial intelligence speakers affect the positive (+) effect on use and user satisfaction. Finally, we tried to determine if it comes with user benefits. As a result of analyzing DeLone and McLean’s IS SUCCESS model, it was found that the information quality provided by the artificial intelligence speaker is used, but it is difficult to deliver satisfaction, but the service and system quality deliver user satisfaction. Moreover, it was found that perceived pleasure had no positive effect on the use or user satisfaction. It is necessary to upgrade to a level that can provide information providing quality and enjoyment that users can perceive. This means that it is a system with built-in voice recognition to answer questions connected to the Internet.
On the Multimodal Resolution of a Search Sequence in Virtual Reality
In virtual reality (VR), participants may not always have hands, bodies, eyes, or even voices—using VR helmets and two controllers, participants control an avatar through virtual worlds that do not necessarily obey familiar laws of physics; moreover, the avatar’s bodily characteristics may not neatly match our bodies in the physical world. Despite these limitations and specificities, humans get things done through collaboration and the creative use of the environment. While multiuser interactive VR is attracting greater numbers of participants, there are currently few attempts to analyze the in situ interaction systematically. This paper proposes a video-analytic detail-oriented methodological framework for studying virtual reality interaction. Using multimodal conversation analysis, the paper investigates a nonverbal, embodied, two-person interaction: two players in a survival game strive to gesturally resolve a misunderstanding regarding an in-game mechanic—however, both of their microphones are turned off for the duration of play. The players’ inability to resort to complex language to resolve this issue results in a dense sequence of back-and-forth activity involving gestures, object manipulation, gaze, and body work. Most crucially, timing and modified repetitions of previously produced actions turn out to be the key to overcome both technical and communicative challenges. The paper analyzes these action sequences, demonstrates how they generate intended outcomes, and proposes a vocabulary to speak about these types of interaction more generally. The findings demonstrate the viability of multimodal analysis of VR interaction, shed light on unique challenges of analyzing interaction in virtual reality, and generate broader methodological insights about the study of nonverbal action.
Analysis of Factors Affecting Use Behavior towards Mobile Payment Apps: A SEM Approach
The main aim of this research study is to examine the impact of five independent variables viz. performance expectancy, effort expectancy, social influence, facilitating conditions, and hedonic motivation on behavioral intention to use mobile payment apps. This study is further aimed at investigating the influence of behavioral intention on the use behavior of mobile payment app users. This study strives to investigate the use behavior of people who have already used mobile payment apps like Google Pay, PhonePe, and PayTM previously for making payments for the products and services they have purchased from various sellers. The researchers used the UTAUT2 theory to examine the relationship between the independent and dependent variables mentioned above. The data was collected from 618 mobile payment app users from Vidarbha, M.S., India. Availability sampling and purposive sampling techniques were adopted for the final selection of the respondents. A structured questionnaire was designed by the researchers for collecting the required primary data. The six proposed hypotheses were tested by using SMART-PLS 3.3.5 software. The results indicated support for all six proposed hypotheses. The proposed model explained a substantial amount of variance in behavioral intention (% and %) in use behavior towards mobile payment apps explained by independent variables. Facilitating conditions exhibited the strongest effect on behavioral intention.
Overviewing Gaming Motivation and Its Associated Psychological and Sociodemographic Variables: A PRISMA Systematic Review
Nowadays, video games are part of our everyday life, and the number of players is increasing each day passing by. Thus, understanding what motivations drive people to play video games is becoming a very important topic for researchers. That is why this systematic review had the objective to summarize the existing literature about gaming motivation by including papers that used a validated tool to do so while excluding those that did address just the psychopathological aspect of gaming. The systematic review was carried out through the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRSIMA). A total of 53 papers were included in this systematic review, and the findings revealed that nonaddicted players and addicted players seem both to play for social, achievement, and competition motivations. Male players appeared more oriented to play to compete with others, while female players seemed to use games for relationship and social reasons. Gaming motivation was stronger in younger people.