AI and Tech in Behavioral and Social Sciences
Volume:3 Issue: 1, Winter 2025

This narrative review explores the role of social media in promoting health-oriented lifestyles and influencing behavioral change from a health management perspective. Social media platforms have emerged as powerful tools for disseminating health information, raising awareness, and engaging diverse audiences in health promotion activities. This review synthesizes findings from various studies to evaluate how social media impacts health behaviors, such as diet, physical activity, and smoking cessation, and highlights the contributions of health influencers and organizations in shaping public perceptions and behaviors. The review also examines the challenges associated with the use of social media for health promotion, including the spread of misinformation, disparities in digital access, and privacy concerns. Theoretical frameworks such as the Social Cognitive Theory and the Health Belief Model are applied to understand the mechanisms through which social media influences behavior. Additionally, emerging trends in AI-driven health content and the integration of social media with other digital health tools, such as mobile apps and telemedicine, are explored for their potential to enhance personalized health interventions. The review concludes with policy recommendations to improve the quality and reliability of health information on social media, emphasizing the need for ethical standards and digital literacy programs to address the digital divide. The findings underscore the importance of strategic management in leveraging social media for public health promotion, while addressing the associated risks, to maximize its potential in fostering healthier communities.

The aim of the present study was to design a model for organizing sports events for individuals with disabilities and veterans in Iran. This research is a mixed-methods study (qualitative-quantitative) with an exploratory approach. The statistical population in the qualitative section consisted of academic experts in the field of sports events for individuals with disabilities and veterans, and in the quantitative section, it included sports managers and the National Paralympic Committee, the Federation of Sports for Veterans and Disabled Persons, as well as the heads and secretaries of the provincial boards of the Federation for Veterans and Disabled Persons. The qualitative sample consisted of 14 experts, selected through purposive and snowball sampling techniques until theoretical saturation was reached. In the quantitative section, between 5 to 10 samples were required for each item, resulting in a total sample size of 384 individuals. The data collection tools included semi-structured interviews in the qualitative section and a researcher-made questionnaire in the quantitative section. Content validity was confirmed by experts, and construct validity was assessed through exploratory and confirmatory factor analyses. Reliability was measured using composite reliability, Cronbach's alpha, divergent and convergent validity, and structural equation modeling. The model's goodness-of-fit index (GOF) was reported as 0.59. Ultimately, the organization of sports events for individuals with disabilities and veterans in Iran was presented with 13 factors: organizing costs and determining financial resources, organizing operational plans for preparation, organizing regulations for competition execution, publishing brochures (advertising and publications), registration and acceptance of participants, organizing medical and pharmacy services, organizing accommodation and event venues, organizing committees and human resources, organizing cultural events and leisure activities, organizing nutrition and catering services for participants, organizing transportation services, organizing opening and closing ceremonies, and organizing referees and volunteers.

Today, financial documents are no longer recorded and stored as paper documents but as digital documents based on the Internet platform. For this reason, the misuse of financial data has increased, leading to extensive research on the secure storage, sharing, and exchange of data. One of the methodologies used in this field is blockchain. To effectively combat these risks, businesses are turning to innovative solutions such as blockchain technology and digital signatures. These advanced technologies provide strong transaction authentication and provide enhanced defense. Digital signatures and blockchain technology work together to increase the security and reliability of digital transactions. Blockchain provides essential elements such as transparency, immutability, and consensus, while digital signatures work to verify authenticity and integrity. Combining these two powerful tools creates a robust solution that creates secure and reliable digital interactions that minimize the risk of fraud and foster trust in the digital ecosystem. This paper explores digital signatures and blockchain technology and explore how they can help improve security. In this study, we have designed a model based on blockchain and digital signature to improve data security by examining existing system models and data exchange security methods. Finally, this research has improved blockchain security for the transfer of financial documents.

The present study aimed to examine the consequences of implementing artificial intelligence in human resource management within Iranian government organizations. The research method, in terms of data type, is mixed-method (qualitative-quantitative); in terms of the research environment, it is library-based; and in terms of data collection method, nature, and research method, it is descriptive-correlational. In this research, interviews were used to identify the consequences. The statistical population in the qualitative section consisted of 5 experts from Iranian government organizations, and in the quantitative section, 335 employees from these organizations. The data collection tool in the qualitative section was interviews, while in the quantitative section, a researcher-made questionnaire based on a five-point Likert scale was used. For data analysis in the quantitative section, Cronbach's alpha tests, Average Variance Extracted (AVE), the AVE square root matrix, and confirmatory factor analysis using smartPLS software were employed. The results showed that the consequences of implementing artificial intelligence in human resource management in Iranian government organizations span five areas: recruitment, training, performance evaluation, compensation, and retention. Moreover, the results indicated that among the components, the recruitment component requires further strengthening.

This narrative review explores the transformative role of Artificial Intelligence (AI) in shaping the evolution of journal impact within the field of sports management. AI has become a critical tool in academic publishing, influencing citation metrics, content curation, and peer review processes. Key metrics such as impact factor, h-index, and citation counts have been significantly impacted by AI-driven tools, which enhance the visibility, accessibility, and interdisciplinary reach of sports management journals. Journals that have adopted AI technologies, such as Sport Management Review and Journal of Sport Management, have experienced accelerated citation growth and improved rankings. This review also highlights how AI enhances research quality by automating literature reviews, data analysis, and predictive modeling, while improving reproducibility and credibility. However, challenges such as biases in AI algorithms, ethical concerns, and the potential overreliance on AI for research evaluation must be addressed to ensure that AI complements rather than replaces human expertise in academic publishing. The review concludes by suggesting future research directions, including the need for ethical AI development, improved transparency, and the exploration of AI’s long-term impact on academic publishing in sports management. Ultimately, AI offers vast potential to drive innovation, elevate research quality, and shape the future of sports management scholarship.

This paper investigates how destructive work behaviors, Organizational Citizenship Behaviors (OCBs), and fiscal decentralization intersect in developing countries' economic development. Corruption and inefficiency in decentralized systems hinder governance and economic growth, while altruism and conscientiousness in organizations foster transparent, collaborative cultures. Giving more power to local governments through fiscal decentralization can lead to better service delivery and meeting the needs of the community, as long as negative behaviors are minimized, and organizational citizenship behaviors are promoted. The essay highlights the significance of focused interventions like anti-corruption efforts and ethics training to improve governance integrity and promote sustainable economic growth.

The purpose of this study is to identify and control credit risk in banks utilizing new supervisory technologies with the neural network algorithm and the random forest algorithm. This research, in terms of its nature and objective, is categorized as theoretical and applied research. Given the quantitative nature of the study and the use of data mining for customer credit scoring, this investigation is data-driven. The primary foundation of this research is the discovery of knowledge from banking databases. In this study, real customers who received credit facilities from Tejarat Bank and Saman Bank in Tehran over a one-year period, whether they returned the loans to the bank or not, were defined as the statistical population. Consequently, for sampling, all individual credit customers of the selected branches of these banks during the specified time frame were examined. Out of 500 credit customers, a simple random sampling method was employed to select those who had received loans during this period, resulting in the selection of 230 samples for this study. After collecting the previous bank customer data from the relevant database and cleaning the data, the influential variables in customer ranking were identified by reviewing previous scientific research. In the next phase, using the neural network algorithm and the random forest algorithm, and with the help of relevant software, customers were classified based on their characteristics, and their behavior was predicted. The findings indicated that the random forest algorithm was more efficient in predicting customer credit risk. Statistical test results showed that the support vector machine model had higher accuracy in predicting customer credit risk. The random forest (DT) algorithm used in this research had the highest accuracy among all models, and with feature selection, the model's accuracy increased compared to the base model, achieving the highest accuracy (81.49%) among all techniques

The increasing demand for sustainability in sports facilities has driven the adoption of green technologies aimed at reducing energy consumption, water usage, and waste generation. These technologies not only contribute to environmental conservation but also improve operational efficiency and reduce long-term costs for facility management. This article reviews the implementation of green technologies in sports facilities and assesses their impact on operational efficiency and sustainability. A comprehensive review of existing literature was conducted, focusing on renewable energy technologies, smart energy management systems, water conservation technologies, waste management innovations, and the use of sustainable materials in the construction and retrofitting of sports facilities. The review also explores the economic, technological, and operational challenges that affect the adoption of these technologies, as well as potential future directions for innovation and policy support. Green technologies, such as solar panels, smart HVAC systems, rainwater harvesting, and waste-to-energy solutions, have been shown to significantly reduce energy and water consumption while minimizing waste output. Despite the high initial costs, the long-term financial savings from reduced utility bills and maintenance costs make these technologies a valuable investment for sports facilities. However, barriers such as the high cost of installation, technological limitations in certain regions, and operational resistance present challenges to widespread adoption. The article highlights the importance of adopting green technologies in sports facilities to achieve both sustainability and operational efficiency. It calls for more research on scalable solutions and policy initiatives to encourage broader implementation, emphasizing the need for collaboration between facility managers, policymakers, and technology providers.

The increasing influence of artificial intelligence (AI) on overall life aspects has led to numerous worries despite its advantages, providing a better quality of life for humans. Therefore, some mechanisms are required to enhance individuals’ trust in computer systems and prevent the adverse autonomous behaviors of intelligent agents. Consideration of the cognitive potentials of humans and use it in intelligent systems is an undeniable principle and shortcut. Therefore, morality and ethics in AI have received attention in theory and practice over recent decades. This study investigates the attempts to develop artificial moral agents based on an engineering viewpoint that focuses on technical aspects. The current challenges and gaps are expressed, and some recommendations are proposed for those interested in further studies in this field.

The objective of the present study is to propose a framework of drivers and strategic factors within the comprehensive model of intellectual capital and competitive advantage in startup companies. This research is applied in nature and employs a descriptive-analytical approach. The methodology is qualitative. The statistical population consists of academic experts and managers of startup companies, from whom 12 individuals were selected using the snowball sampling method until reaching theoretical saturation. The data collection tool is a semi-structured interview developed based on theoretical foundations. Data analysis was conducted using thematic analysis. The findings of the qualitative phase of the research are explained in the form of two main categories and eight subcategories. The identified drivers include the following indicators: human capital, innovation capital, customer capital, and infrastructure capital. The identified strategic factors are as follows: achieving balance and optimizing the collaboration-competition mix in the industry, membership in science and technology parks and incubators, employing lean production processes, and implementing technology-based competitive strategies.

Although many of these insights address broader aspects of supply chain management and export competition among companies, such research lacks empirical evidence regarding the value acquisition of capabilities in companies under study concerning bargaining power and negotiation processes of companies active in the global value chain. Moreover, there is limited assessment of the impact of these factors on the export performance of manufacturing companies, particularly in the machine-made carpet industry. The present study aimed to evaluate the importance and factors influencing the export performance of machine-made carpet companies using knowledge domain analysis and the fuzzy network analysis model. The research methodology was theoretical-applied in terms of purpose, based on a survey research design and descriptive-inductive reasoning. Initially, key metrics affecting export performance were identified through knowledge domain analysis and qualitative content analysis. Subsequently, using a Delphi survey approach, 17 experts and specialists in the export field were selected through non-probability sampling. The fuzzy network multi-criteria analysis model was then employed to evaluate and refine the most effective metrics for measuring variables and to develop the proposed final model.

Artificial intelligence (AI) is a powerful tool that can make accounting processes faster, more accurate, and more efficient. AI can perform repetitive and tedious tasks, reduce human error, enable continuous auditing, and enhance the security and accuracy of audits. However, AI also presents challenges. The cost of acquiring and training AI-based systems can be high. Additionally, AI may lead to a reduction in accounting jobs. AI requires human oversight and control to prevent misuse and fraud. AI may also encounter ethical, legal, and tax-related issues. Furthermore, AI in accounting requires continuous development and updating to align with new needs and environmental changes. AI, due to its capacity to improve and transform the way activities are carried out in this field, is rapidly changing the reality of accounting. Over the years, accounting has significantly transformed from paper-and-pencil work to the use of computers. More importantly, accounting has evolved with programs that reduce time spent on repetitive tasks and decrease the occurrence of errors. The interest in AI solutions in this field is not new, but in recent years, researchers' attention has increasingly focused on it. Despite technological advancements, there appears to be insufficient data to support companies' willingness to integrate AI solutions into their accounting activities. An important aspect of this reality is the ability of professionals to adapt more quickly to the current situation and acquire the necessary skills to work with AI solutions, overcoming the fear of job loss. This article focuses on understanding the impact of AI solutions on accounting by conducting a qualitative study based on a review of the relevant literature from previous years. It highlights the potential changes that AI could bring to accounting professions and the necessary actions to prepare for the emergence of new jobs where AI solutions will play a larger role.

The present study aimed to identify the new challenges and emerging issues in the virtual space during the COVID-19 pandemic in order to provide a comprehensive picture and perspective of this area. This qualitative research utilized a systematic review method. The research population consisted of all articles (179 articles) presented in the last five years on the challenges of virtual space and related fields in specialized and scientific databases. The sample for the study included 20 articles, selected purposefully based on thematic monitoring and theoretical saturation of the data. The research data were gathered through qualitative analysis of the documents under study. Through data analysis, the new challenges and emerging issues of the virtual space during the COVID-19 pandemic were categorized into 4 dimensions, 11 factors, and 56 categories. These included the dimension of socio-cultural challenges (identity disorder, value conflicts, social isolation); environmental challenges (changes in school work culture, transformation of organizational and product competitive advantages, emergence of educational inequities); ethical challenges (violation of privacy, cyber harassment, distortion of informational content); and educational challenges (incomplete teaching of laboratory courses, weakened sense of educational presence, educational frustration).

Consumer buying behavior involves the process of selecting, purchasing, and utilizing goods and services based on individual needs and desires. This behavior is influenced by various factors and can manifest in diverse ways. Despite the extensive research conducted in this field, there remains a gap in the literature concerning a comprehensive survey that covers a broad spectrum of studies and categorizes them into distinct domains. Addressing this gap, our study endeavors to provide a comprehensive overview by surveying and categorizing studies on impulse buying, panic buying, and hoarding. Additionally, we explore the impact of artificial intelligence (AI), customer satisfaction and loyalty, as well as green purchase behavior. Through this endeavor, we aim to offer valuable insights into the multifaceted nature of consumer behavior, thereby contributing to a deeper understanding of the subject and informing future research and practical applications in the field of marketing and consumer studies.

This review aims to explore the role of artificial intelligence (AI) in modern media communications, analyzing its historical evolution, current applications, benefits, challenges, and future prospects. A descriptive analysis approach was employed to synthesize relevant literature and recent studies on AI in media communications, focusing on articles published between 2019 and 2025. The review encompasses a comprehensive analysis of various AI applications across media sectors, including content creation, journalism, marketing, social media, and immersive technologies. Sources include peer-reviewed journal articles, industry reports, and case studies to provide a holistic view of AI’s impact and potential in the field. The review identifies significant advancements in AI technologies, such as machine learning, natural language processing, and generative AI, that have transformed content creation, personalization, and audience engagement. AI-driven tools enable automated content production, data-driven decision-making, and the enhancement of immersive media experiences. However, challenges such as ethical concerns (bias, misinformation, privacy), job displacement, and technological dependence remain prominent. The integration of AI has led to both operational efficiencies and innovative business models, while also necessitating careful regulation to address these emerging risks. AI’s integration into media communications offers transformative benefits, including increased efficiency, creativity, and audience engagement. However, it also raises critical ethical and operational challenges that must be addressed through regulatory frameworks and workforce adaptation. Future advancements in AI, including generative models and immersive technologies, are expected to further reshape media landscapes, presenting new opportunities and challenges for the industry.

With the rapid advancement of human-centric artificial intelligence, this technology has become a significant factor influencing various fields, including medicine, industry, transportation, commerce, law, and banking. Machine-based artificial intelligence, through automated decision-making, has taken over tasks that were previously performed by humans. However, a fundamental challenge in this area is determining legal liability in cases where AI systems make errors. The significance of this issue lies in the fact that, in many countries, traditional civil liability laws are primarily based on human will and actions, and a comprehensive legal framework for artificial intelligence and automated decision-making has yet to be developed. This paper examines the theoretical foundations of civil liability in automated decision-making and the emerging challenges in this domain. This study employs an analytical-descriptive and comparative legal research method. Different legal systems have adopted varying approaches to determining AI civil liability. In the United States, liability is primarily assessed under product liability and vicarious liability doctrines, whereas the European Union is moving towards a strict liability model and AI civil liability insurance. In Iran, civil liability remains based on human and corporate legal personality, and AI lacks independent legal personality. Some legal systems, such as Germany, have proposed that AI-based decision-making should be subject to a corporate liability model. This paper analyzes various models for determining AI civil liability, including developer and manufacturer liability, operator liability, strict liability, civil liability insurance, and the possibility of granting AI limited legal personality. The strict liability model, which is gaining traction in the European Union, holds organizations accountable for AI-related damages, regardless of fault. In Iran, the absence of specific regulations in this area may lead to significant legal ambiguities and enforcement challenges in AI-related lawsuits. Finally, this paper emphasizes the necessity of drafting new legal regulations aligned with international standards to effectively address the legal challenges and civil liability issues arising from AI-driven automated decision-making.