Human Behavior and Emerging Technologies
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Acceptance rate28%
Submission to final decision63 days
Acceptance to publication25 days
CiteScore5.800
Journal Citation Indicator3.280
Impact Factor-

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 Journal profile

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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This journal's articles appear in a wide range of abstracting and indexing databases, and are covered by numerous other services that aid discovery and access. Find out more about where and how the content of this journal is available.

Latest Articles

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Research Article

The Critical Success Factors for Sustainability Financial Technology in Vietnam: A Partial Least Squares Approach

This paper helps in determining the critical success factors (CSFs) for sustainability financial technology business. An attempt is made to study the influence of were found CSFs to be key aspects of a business that must go well to ensure the success of an organization. These CSFs include issues that are vital to a company’s operating activities and its future success. The study collected data from 253 respondents through a structured questionnaire. Partial Least Square Structural Equation Modelling has been adopted to analyze the data through SmartPLSv3. For better understanding, we emphasize that companies within the same industry may exhibit different CSFs due to anomalies in their environmental situation or strategic situations, which might pose as a challenge for this study and the future exploration towards a general set of CSFs for sustainability fintech companies. The research concluded the presented seven CSFs with the following customer centricity, low profit margin, agility, scalability, security management, innovation, and compliance easy. The findings of the paper are beneficial for fintech enterprises and marketers to enhance the awareness and advantages of financial technology according to the needs of consumers and add value to the existing literature on the future sustainable financial technology businesses.

Research Article

The Impact of Emotional Intelligence on Internet Addiction: A Case Study of Vietnamese Students

Internet addiction has attracted significant attention due to its adverse effects on humans, especially young people. This study is aimed at investigating the impact of emotional intelligence on Internet addiction. Data was collected from 744 Vietnamese students in Vietnam. SPSS 20.0 software was used for descriptive statistics, reliability testing, factor analysis, and regression. The empirical results showed that emotional intelligence had a negative influence on Internet addiction. Specifically, the components self-emotion appraisal (SEA), others’ emotion appraisal (OEA), and regulation of emotion (ROE) significantly affected Internet addiction. However, the effect of the component use of emotion (UOE) on Internet addiction was not found to be statistically significant. Overall, the results of the study indicate that improving emotional intelligence may reduce the extent of Internet addiction among Vietnamese students.

Research Article

Can Your Smartphone Make You a Tourist? Mine Does: Understanding the Consumer’s Adoption Mechanism for Mobile Payment System

Payment through mobile phones is a vital breakthrough in the arena of online businesses and e-commerce. The purpose of this study was to investigate the determinants, enablers, and barriers involved in the success or failure of the mobile payment system (MPS) for the travel industry. The study employs the constructs operationalized from coping theory, unified theory of acceptance and use of technology (UTAUT 2), and innovation resistance theory (IRT). The data has been collected from the 378 travelers who have used MPS for travel bookings for the first time. The customers of various travel agencies have been approached using an online questionnaire. Data has been analyzed using Statistical Package for Social Sciences (SPSS) and Analysis of Moment Structures (AMOS) version 26. The analysis revealed several interesting findings. All of the direct hypotheses for coping theory constructs were accepted except for the mediating role of satisfaction. However, the factors of UTAUT 2 and IRT revealed very thought-provoking findings, questioning various obvious perceptions. The findings of the study can be used by the media agencies, hotels, and travel and tourism departments of the governments, especially in the context of developing nations.

Review Article

Emotionally Intelligent Chatbots: A Systematic Literature Review

Conversational technologies are transforming the landscape of human-machine interaction. Chatbots are increasingly being used in several domains to substitute human agents in performing tasks, answering questions, giving advice, and providing social and emotional support. Therefore, improving user satisfaction with these technologies is imperative for their successful integration. Researchers are leveraging Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques to impart emotional intelligence capabilities in chatbots. This study provides a systematic review of research on developing emotionally intelligent chatbots. We employ a systematic approach to gather and analyze 42 articles published in the last decade. The review is aimed at providing a comprehensive analysis of past research to discover the problems addressed, the techniques used, and the evaluation measures employed by studies in embedding emotion in chatbot conversations. The study’s findings reveal that most studies are based on an open-domain generative chatbot architecture. Researchers mainly address the issue of accurately detecting the user’s emotion and generating emotionally relevant responses. Nearly 57% of the studies use an enhanced Seq2Seq encoding and decoding of the input of the conversational model. Almost all the studies use both the automatic and manual evaluation measures to evaluate the chatbots, with the BLEU measure being the most popular method for objective evaluation.

Research Article

TARNet: An Efficient and Lightweight Trajectory-Based Air-Writing Recognition Model Using a CNN and LSTM Network

Air-writing is a growing research topic in the field of gesture-based writing systems. This research proposes a unified, lightweight, and general-purpose deep learning algorithm for a trajectory-based air-writing recognition network (TARNet). We combine a convolutional neural network (CNN) with a long short-term memory (LSTM) network. The architecture and applications of CNN and LSTM networks differ. LSTM is good for time series prediction yet time-consuming; on the other hand, CNN is superior in feature generation but comparatively faster. In this network, the CNN and LSTM serve as a feature generator and a recognizer, optimizing the time and accuracy, respectively. The TARNet utilizes 1-dimensional separable convolution in the first part to obtain local contextual features from low-level data (trajectories). The second part employs the recurrent algorithm to acquire the dependency of high-level output. Four publicly available air-writing digit (RealSense trajectory digit), character (RealSense trajectory character), smart-band, and Abas datasets were employed to verify the accuracy. Both the normalized and nonnormalized conditions were considered. The use of normalized data required longer training times but provided better accuracy. The test time was the same as those for nonnormalized data. The accuracy for RTD, RTC, smart-band, and Abas datasets were 99.63%, 98.74%, 95.62%, and 99.92%, respectively.

Research Article

The Effects of Basic Psychological Needs, Task–Technology Fit, and Student Engagement on MOOC Learners’ Continuance Intention to Use

Massive open online courses (MOOCs) continue to remain in the spotlight as a promising future education environment. However, more than 80% of learners stop learning before attending one-third of the course. Despite a continuous spread of MOOC and high dropout rate, little has examined the antecedent factors that influence student engagement in technology enhanced MOOC learning environment from the Job Demand-Resources (JD-R) model. The purpose of this study was to empirically identify the effects of individuals’ basic psychological needs and the task–technology fit on MOOC learners’ continuance intention to use, as well as the mediating effect of student engagement in MOOCs. Based on survey data from 201 Korean-MOOC learners, structural equation modeling was employed to assess the model. The findings are as follows: The basic psychological needs in MOOCs did not directly affect continuance intention to use, but did affect student engagement; the task–technology fit of MOOCs directly affected continuance intention to use and student engagement; and student engagement in MOOCs mediated between the basic psychological needs and task–technology fit, and continuance intention to use. It directly affected continuance intention to use. Implications were suggested for designing courses in MOOCs to increase student engagement for continuance intention to use.

Human Behavior and Emerging Technologies
Publishing Collaboration
More info
Wiley Hindawi logo
 Journal metrics
See full report
Acceptance rate28%
Submission to final decision63 days
Acceptance to publication25 days
CiteScore5.800
Journal Citation Indicator3.280
Impact Factor-
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Article of the Year Award: Outstanding research contributions of 2021, as selected by our Chief Editors. Read the winning articles.