Javascript is required
Search
/
/
MindVanguard: Beyond Behavior
MITS
MindVanguard: Beyond Behavior (MVBB)
OCS
ISSN (print): 3005-7965
ISSN (online): 3005-7973
Submit to MVBB
Review for MVBB
Propose a Special Issue
Current State
Issue
-
Volume
-
Archive
Home

The MindVanguard: Beyond Behavior (MVBB) is at the forefront of exploring human behavioral sciences, delving into how digital innovations and global cultural shifts shape behavior. Emphasizing the role of artificial intelligence and cognitive advancements, it seeks thought-provoking manuscripts that challenge norms and translate theories into practice. The journal aims to be a hub for scholars and practitioners redefining behavioral science. Published quarterly by Acadlore, the journal typically releases its four issues in March, June, September, and December each year.

  • Professional Service - Every article submitted undergoes an intensive yet swift peer review and editing process, adhering to the highest publication standards.

  • Prompt Publication - Thanks to our proficiency in orchestrating the peer-review, editing, and production processes, all accepted articles see rapid publication.

  • Open Access - Every published article is instantly accessible to a global readership, allowing for uninhibited sharing across various platforms at any time.

Editor(s)-in-chief（1）
kesra nermend
University of Szczecin, Poland
kesra.nermend@usz.edu.pl | website
Research interests: Quantitative Methods and Computer Tools in Socio-Economic Analysis; Multi-Criteria Methods in Data Analysis; Cognitive Neuroscience Techniques in Social Behavior Study; Modeling of Consumer Preferences in Business Decision-Making

Aims & Scope

Aims

MindVanguard: Beyond Behavior (MVBB) endeavors to forge new frontiers in the exploration and advancement of human behavioral sciences. Committed to surpassing the traditional scope of academic publications, the myriad influences shaping human behavior, ranging from digital innovations to global socio-cultural shifts, are probed. The spotlight is on how artificial intelligence, global interconnectedness, cultural dynamics, and cognitive advancements are coalescing to redefine behavioral sciences. Manuscripts that provoke thought, challenge the status quo, broaden the knowledge spectrum, and translate complex theories into practical applications are particularly sought after. Envisioned as a melting pot for scholarly and pragmatic discourse, the journal aims to galvanize a global community of thinkers at the vanguard of behavioral science.

Furthermore, MVBB highlights the following features:

  • Every publication benefits from prominent indexing, ensuring widespread recognition.

  • A distinguished editorial team upholds unparalleled quality and broad appeal.

  • Seamless online discoverability of each article maximizes its global reach.

  • An author-centric and transparent publication process enhances submission experience.

Scope

MVBB's expansive scope encompasses, but is not limited to:

  • Technological Impact on Behavioral Science: Assessing IT breakthroughs, including AI, machine learning, and analytics, and their transformative effects on understanding and shaping human behavior.

  • Cultural and Social Dynamics: Examining the role of societal evolution and cultural heterogeneity in molding cognitive and social behaviors, and promoting inclusive research dialogues.

  • Neurobehavioral Foundations: Investigating the neurology and genetics underpinning behavior to harmonize biological, psychological, and behavioral sciences.

  • Behavioral Development in Various Environments: Scrutinizing the influence of both natural and urban settings on behavioral growth and the mental repercussions of ecological changes.

  • Application of Behavioral Economics: Applying behavioral economics to inform and influence policy formulation, strategic business planning, and economic theory crafting.

  • Ethics in Behavioral Inquiry: Evaluating the ethical landscape of behavioral research, with a particular focus on cognitive autonomy and the ethics of behavioral influence.

  • Mental Health in Contemporary Society: Addressing the behavioral dimensions of mental health, coping mechanisms, and general well-being in today's fast-paced lifestyle.

  • Impact of Educational and Behavioral Strategies: Assessing the impact of pedagogical and behavioral strategies within diverse educational and social frameworks.

  • Integrative Research Methodologies: Promoting an amalgamation of methodological approaches from disparate disciplines to deepen the richness of behavioral research.

  • Prognosticating Behavioral Evolution: Forecasting the challenges and directional shifts in behavior due to evolving technological landscapes, societal transformations, and environmental developments.

Articles
Recent Articles
Most Downloaded
Most Cited

Abstract

Full Text|PDF|XML
This study evaluates the influence of brands listed in the Borsa Istanbul (BIST) Sustainability 25 Index on fostering sustainable consumption behaviors, a critical issue in contemporary society. An analysis was conducted on the sustainability-related content from these brands' websites and Instagram accounts. The BIST Sustainability Index, which serves as a benchmark for companies in Türkiye to develop policies related to environmental, social, and corporate governance (ESG) risks, was utilized to select the sample. This index plays a pivotal role in informing responsible investors about corporate sustainability practices. The investigation primarily focused on how these brands communicate sustainability on their Instagram accounts through detailed content analysis. It was observed that, while comprehensive information on sustainability initiatives is presented on corporate websites, this communication is not adequately reflected on Instagram platforms. Given the mandatory disclosure of sustainability activities by companies listed in the BIST Sustainability 25 Index, the importance of effective communication on social media, in addition to website information dissemination, is underscored. Among the brands, Arçelik was identified as the most active in sharing sustainability-related posts on Instagram. Although these posts received a considerable number of likes, they garnered minimal user engagement in terms of comments. The study reveals a discrepancy between the intensity of sustainability activities undertaken by these indexed companies and their representation on social media channels. Consequently, it is recommended that these businesses place a greater emphasis on incorporating sustainability themes within their social media marketing communications. This study underscores the need for a more robust digital media strategy to reflect sustainability efforts accurately, thereby contributing to the broader discourse on sustainable consumption and the efficacy of digital marketing.

Abstract

Full Text|PDF|XML
The assessment of driving behavior, vital for ensuring passenger safety and optimizing resource utilization in transportation systems, faces challenges due to inherent unpredictability and complexity. This study addresses these challenges by introducing innovative methodologies for the extraction, classification, and prediction of diverse driving patterns, utilizing data from "On Board Diagnostics" (OBD) ports in modern vehicles. In this approach, a comprehensive suite of advanced Machine Learning (ML) and Deep Learning (DL) stechniques, including Convolutional Neural Networks (CNNs), Optimized Spectral Neural Classification (OSNCA), and Fuzzy Logical Feature Selection (FLFS), are employed. These techniques are instrumental in overcoming limitations of previous models, enhancing accuracy in driving behavior evaluation. The utilization of FLFS in conjunction with OSNCA represents a novel method in driver behavior analysis. By applying these techniques, driver characteristics and behaviors are systematically categorized into distinct classes, facilitating a nuanced understanding of driving dynamics. The integration of these advanced methodologies not only furthers the analysis of driver behavior but also significantly improves classification and prediction capabilities. This research contributes to the development of safer, more efficient transportation networks by offering a refined approach to the analysis, categorization, and prediction of driver behavior, thereby advancing the field of driving behavior analysis.
- no more data -
- no more data -
- no more data -