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Behavioral Sciences
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Paving the Path to Well-Being Through Human Behavior Analysis with...
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Paving the Path to Well-Being Through Human Behavior Analysis with Data Science

A special issue of Behavioral Sciences (ISSN 2076-328X). This special issue belongs to the section "Developmental Psychology".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2763

Special Issue Editors

Dr. Kota Kodama

E-Mail Website
Guest Editor
OIC Research Organization, Ritsumeikan University, Osaka 567-8570, Japan
Interests: technology management; innovation management; pharmaceutical industry; public health
Special Issues, Collections and Topics in MDPI journals
Dr. Makoto Niwa

E-Mail Website
Guest Editor
Nippon Shinyaku Co., Ltd., Kyoto 601-8550, Japan
Interests: life sciences; pharmaceutical chemistry
Special Issues, Collections and Topics in MDPI journals
Dr. Itsuki Kageyama

E-Mail Website
Guest Editor
Medical Data Science Lab, Hoshi University, Tokyo 142-8501, Japan
Interests: digital health; mobile health

Special Issue Information

Dear Colleagues,

In recent years, the concept of well-being has garnered significant attention across various disciplines. As societies strive for an improved quality of life, understanding the factors that contribute to well-being becomes crucial. Concurrently, advancements in data science have opened new avenues for analyzing human behavior in ways that were previously unimaginable. This Special Issue aims to bridge these two domains, leveraging the power of data science to uncover insights into human behavior that can inform strategies for enhancing well-being.

Scope and Objectives:

This Special Issue deals with well-being in relation to quality of work, self-actualization, recreation, exercise, and mindfulness, using various frameworks and datasets, to focus on the structural aspects of human behavior analysis, encouraging the use of modern metrics and methodologies, such as structural equation modeling (SEM), network analysis, large-scale data analysis and artificial intelligence (AI), to explore the intricate relationships between various behavioral factors and well-being outcomes.

Topics of Interest:

-Applications of structural equation modeling in well-being research;
-Network analysis of social behaviors and their impact on well-being;
-AI-driven approaches to predicting and enhancing well-being;
-Case studies demonstrating the integration of data science and behavioral analysis;
-Scientometric approach to well-being research advancements;
-Methodological advancements in the measurement and analysis of well-being;
-Cross-cultural studies on well-being using data science techniques.

Dr. Kota Kodama
Dr. Makoto Niwa
Dr. Itsuki Kageyama
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Behavioral Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • well-being
  • job satisfaction
  • data analysis
  • questionnaire survey
  • biometric data

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

23 pages, 1848 KiB  
Article
Cross-Analysis of mHealth Social Acceptance Among Youth: A Comparative Study Between Japan and China
by Olugbenga Akiogbe, Hanlin Feng, Karin Kurata, Makoto Niwa, Jianfei Cao, Shuo Zhang, Itsuki Kageyama, Yoshiyuki Kobayashi, Yeongjoo Lim and Kota Kodama
Behav. Sci. 2025, 15(2), 213; https://doi.org/10.3390/bs15020213 - 14 Feb 2025
Viewed by 652
Abstract
Although mobile health (mHealth) technologies have emerged as a revolutionary approach to enhance healthcare delivery, few studies have examined how it is perceived and accepted in different cultures. This study investigated mHealth’s social acceptance among young people in Japan and China, with a [...] Read more.
Although mobile health (mHealth) technologies have emerged as a revolutionary approach to enhance healthcare delivery, few studies have examined how it is perceived and accepted in different cultures. This study investigated mHealth’s social acceptance among young people in Japan and China, with a focus on cultural influences on technology adoption. A comparative analysis approach was adopted, employing an extended unified theory of acceptance and use of the technology model. University students from both countries, recruited using harmonized sampling methods, completed questionnaires. We employed descriptive statistics to summarize the sample characteristics, confirmatory factor analysis to validate the constructs, multigroup analysis to test for measurement invariance and ensure the applicability of the model in both cultural contexts, and comparative path analysis to explore differences in the various factors influencing mHealth acceptance in each cultural setting. The findings revealed distinct cultural effects on mHealth acceptance. Japanese young people showed cautious acceptance influenced by societal norms and infrastructure, whereas Chinese young people demonstrated strong engagement driven by government support and the growing digital health industry. The study emphasizes the importance of considering cultural and systemic dynamics when integrating mHealth into youth healthcare models and suggests tailored strategies for successful implementation. Full article
(This article belongs to the Special Issue Paving the Path to Well-Being Through Human Behavior Analysis with Data Science)
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26 pages, 4437 KiB  
Article
Research on the Impact of an AI Voice Assistant’s Gender and Self-Disclosure Strategies on User Self-Disclosure in Chinese Postpartum Follow-Up Phone Calls
by Xinxin Sun, Tianyuan Shen, Qianling Jiang and Bin Jiang
Behav. Sci. 2025, 15(2), 184; https://doi.org/10.3390/bs15020184 - 10 Feb 2025
Viewed by 1707
Abstract
This study examines the application of AI voice assistants in Chinese postpartum follow-up phone calls, with particular focus on how interaction design strategies influence users’ self-disclosure intention. A 2 (voice gender: female/male) × 3 (self-disclosure strategies: normal conversation without additional disclosure/objective factual disclosure/emotional [...] Read more.
This study examines the application of AI voice assistants in Chinese postpartum follow-up phone calls, with particular focus on how interaction design strategies influence users’ self-disclosure intention. A 2 (voice gender: female/male) × 3 (self-disclosure strategies: normal conversation without additional disclosure/objective factual disclosure/emotional and opinion-based disclosure) mixed experimental design (n = 395) was conducted to analyze how the gender and self-disclosure strategies of voice assistants affect users’ stereotypes (perceived warmth and competence), and how these stereotypes, mediated by privacy calculus dimensions (perceived risks and perceived benefits), influence self-disclosure intention. The experiment measured various indicators using a 7-point Likert scale and performed data analysis through analysis of variance (ANOVA) and structural equation modeling (SEM). The results demonstrate that female voice assistants significantly enhance users’ perceived warmth and competence, while emotional self-disclosure strategies significantly improve perceived warmth. Stereotypes about the voice assistant positively affect users’ self-disclosure intention through the mediating effects of perceived risk and benefit, with perceived benefit exerting a stronger effect than perceived risk. These findings provide valuable insights for the design and application of AI voice assistants in healthcare, offering actionable guidance for enhancing user interaction and promoting self-disclosure in medical contexts. Full article
(This article belongs to the Special Issue Paving the Path to Well-Being Through Human Behavior Analysis with Data Science)
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