AI and Tech in Behavioral and Social Sciences

The Effect of Generative AI-Based Digital Marketing Communication Personalization on Customer Trust, Customer Experience, and Repurchase Intention: Evidence from Iranian Online Store Customers

Homa Doroudi ^* iD , Seyedeh Aynaz Jabbari iD , Yasser Heidari iD

With the expansion of e-commerce and the growing role of artificial intelligence in customer interactions, understanding the effects of AI-enabled personalization has become important for online retail performance. This applied, descriptive-survey study examined the effect of generative AI-based personalization of digital marketing communication on customer trust, customer experience, and repurchase intention among customers of Iranian online stores. Data were collected from 384 online customers using a researcher-developed 39-item questionnaire. Content validity was assessed through expert review, sampling adequacy through KMO statistics, reliability through Cronbach's alpha, and structural relationships through structural equation modeling using LISREL 8.8. The results showed that generative AI-based personalization had positive and significant effects on customer trust, customer experience, and repurchase intention. Customer experience and customer trust also showed positive direct associations with repurchase intention, and customer trust significantly strengthened the relationship between customer experience and repurchase intention. The findings suggest that generative AI-based personalization can improve online customer relationships when it is perceived as relevant, trustworthy, and supportive of a positive customer experience.

Intelligent Design and Validation of an Administrative Automation Evaluation Model in Government Organizations: An Exploratory Mixed-Methods Study

Fatemeh Mottaghi iD , Behzad Souki ^* iD , Zahra Moghimi iD

This study designed and validated a multidimensional model for evaluating administrative automation in government organizations. An exploratory mixed-methods design was used. In the qualitative phase, content analysis of documents and expert interviews was used to identify evaluation indicators; the resulting pool was refined through a two-round Delphi procedure with 20 experts. In the quantitative phase, a 41-item questionnaire was administered to 127 public-sector managers and experts familiar with administrative automation systems. Partial least squares structural equation modeling (PLS-SEM) was used to assess the measurement and structural models, and an adaptive neuro-fuzzy inference system (ANFIS) was trained to capture nonlinear prediction patterns. The qualitative phase yielded 100 operational indicators, 30 components, and 10 final dimensions. Delphi consensus exceeded 80% for all dimensions, with the highest consensus for process performance and efficiency (93%) and the lowest for sustainability and environmental impacts (80%). PLS-SEM showed statistically significant effects for all ten predictors of the automation evaluation construct, although negative coefficients for selected constructs require cautious methodological interpretation. Model fit was acceptable (SRMR = 0.041; NFI = 0.91). The ANFIS test results indicated moderate-to-good predictive performance (RMSE = 0.464; R² = .626), with strategic alignment and public value, technology maturity and scalability, user experience and human resources, and financial outcomes emerging as the most influential inputs. The findings indicate that successful administrative automation depends on the simultaneous alignment of strategy, technology maturity, process redesign, human capacity, financial value, and governance safeguards.

Designing a Paradigmatic Model for Boxing Development in Iraq: A Grounded Theory Approach and Data-Based Analysis

Ahmed Salah AbdulAmeer Nassrullah iD , Abbas Naghizadeh-Baghi ^* iD , Farzad Nobakht iD , Nasrin Azizian Kohan iD

This qualitative study designed a paradigmatic model for the development of boxing in Iraq. Boxing has considerable potential to support social cohesion, youth development, economic activity, women’s empowerment, and sport diplomacy, yet its development in Iraq remains constrained by governance weaknesses, resource shortages, infrastructural deficits, socio-cultural barriers, weak planning, and limited monitoring systems. The study used grounded theory based on the Strauss and Corbin approach. Data were collected through semi-structured interviews with 20 participants, including boxing coaches, Iraqi Boxing Federation officials, university academics, and international boxing referees. Purposive and snowball sampling continued until theoretical saturation was reached. The interview data were coded and analyzed using MAXQDA. The analysis produced 153 open codes, 34 axial codes, and 14 selective categories. The findings showed that boxing development in Iraq is shaped by causal conditions, contextual conditions, intervening conditions, strategies, and consequences. Causal conditions included weak governance and resource deficits. Contextual conditions included socio-cultural barriers and insufficient educational and physical infrastructure. Intervening conditions included weak planning, coordination, control, and evaluation. Recommended strategies included infrastructure development, human resource development, strategic governance, international cooperation, digital systems, school-based talent identification, media campaigns, and cultural promotion. Expected consequences included social cohesion, employment generation, improved international representation, sport diplomacy, women’s participation, professional league development, and better athletic performance. The model indicates that boxing development in Iraq cannot be achieved through fragmented interventions. Rather, it requires integrated, simultaneous, and coordinated action across institutional, cultural, financial, educational, and performance domains.

A Structural Model of Classic Film Noir Within the SLP Framework: A Systematic Content Analysis of the Interaction Among Visual, Narrative, and Character Structures

Seyed Amirreza Hashemi iD , Ehsan Shahghasemi ^* iD , Mohammad Reza Hosseini iD

Classic film noir is one of the most influential stylistic movements in the history of cinema, distinguished by visual darkness, complex narrative structures, and morally ambiguous characters. Although this genre has been widely examined, much existing scholarship has treated its visual, narrative, and character dimensions separately, with limited attention to the systematic interaction among these structural levels. This study proposes a preliminary structural model of classic film noir within the SLP framework and examines the co-occurrence of visual style, narrative logic, and characterization. Methodologically, the study adopts a quantitative-analytical design based on systematic content analysis. The research population consisted of classic American film noir productions from the 1940s and 1950s, from which 100 prominent films were selected through purposive sampling. Data were collected through direct observation of the films and binary coding of indicators related to visual style, narrative logic, and characterization. Intercoder reliability was assessed using Cohen's kappa coefficient (κ = .81). The findings showed that characterization indicators had the highest mean presence (66.2%), followed by visual style (63.7%) and narrative logic (60.6%). High-contrast lighting, narrative suspense, and the presence of the antihero were among the most frequently observed indicators. Phi correlation analysis indicated that several pre-defined indicators appeared in clustered co-occurrence patterns. These results suggest that classic film noir may be understood as a multilayered system shaped by the association of visual, narrative, and character elements. The proposed model is therefore interpreted as an exploratory content-analysis model rather than as a fully validated causal or computational model.

Challenges in Harmonizing AI-Enabled Smart Contracts with the General Principles of Contract Law in the Iranian Legal System: A Digital Governance Perspective

Hamid Kaboudjai ^* iD

This study examines the challenges of harmonizing AI-enabled smart contracts with the general principles of contract law in the Iranian legal system from a digital governance perspective. Unlike purely blockchain-based smart contract code, AI-enabled smart contracts may involve algorithmic tools in contract drafting, party verification, oracle-based factual assessment, performance monitoring, risk scoring, or dispute-resolution support. This distinction is central because ordinary smart contract code is typically deterministic, whereas AI systems may generate probabilistic or adaptive outputs that complicate the attribution of consent, responsibility, and legal effect. The study adopts a doctrinal and descriptive-analytical method, relying on Iranian statutory materials, contract-law doctrine, selected comparative scholarship, and leading literature on smart contracts, blockchain governance, and algorithmic accountability. The analysis shows that smart contracts may fall within the scope of Iranian contract law when they operate as electronic instruments for expressing and executing human intention. However, serious challenges remain regarding the verification of legal identity and capacity in pseudonymous blockchain environments, the formation of informed consent through code, the uncertain legal status of cryptocurrencies and tokenized assets, the tension between automated execution and mandatory legal rules, and civil liability for technical or algorithmic failures. The study argues that effective recognition of AI-enabled smart contracts in Iran requires a layered governance model: reliable digital identity mechanisms, human-readable contractual terms accompanying executable code, clear rules on digital assets, auditable and legally responsive code architecture, professional standards for developers, and judicial or arbitral mechanisms capable of reviewing technical execution without undermining innovation. These reforms can help integrate smart contracts into Iran’s legal system while preserving contractual justice, public policy, and legal certainty.

Designing a Model for the Development of Sport for All Through Modern Technologies in Iraq

Jassem Jabbareh Jassem Al-Tamimi iD , Nasrin Azizian Kohan ^* iD , Abbas Naghizadeh Baghi iD , Mehrdad Moharramzadeh iD

The present study aimed to design a model for the development of sport for all through modern technologies in Iraq. This study sought to identify the influencing factors, executive strategies, and consequences of using modern technologies to expand public participation in sport. This study was applied in terms of purpose and qualitative in terms of data nature. It was conducted using a grounded theory approach based on the Strauss and Corbin model. The research population included sport management experts, university professors with managerial or executive experience, senior managers of sport and youth departments, and managers of sports federations. Purposeful sampling was used and continued until theoretical saturation was reached. Data were collected through semi-structured interviews with 15 experts. Open, axial, and selective coding were used to analyze the data, and the criteria of credibility, transferability, dependability, and confirmability were applied to evaluate data quality. The findings showed that the causal factors of the model included lifestyle, target population, and the need of sport for all for modern technologies. The contextual factors were identified in four areas: infrastructural, managerial, cultural, and geographical. Economic, political, and socio-cultural factors were also identified as intervening factors. The strategies of the model included educational, managerial, cultural, research-based, and financial strategies. The consequences of the model were also classified into five dimensions: sport development, knowledge development, economic development, health development, and international development. The results showed that modern technologies can provide the basis for the sustainable development of sport for all in Iraq by increasing awareness, facilitating access to sports services, improving information management, and strengthening public participation. Therefore, strengthening technological infrastructures, educating managers and citizens, financially supporting companies active in sport technology, and coordinating sports, educational, and media institutions are necessary in Iraq’s sport-for-all policymaking.

An Applied Artificial Intelligence Model for Social Media-Based Natural Crisis Management in Iran

Parinaz Mohajerani iD , Mahdi Zare ^* iD , Akbar Nasrollahi Kasmani iD

Natural hazards in Iran frequently generate complex communication demands because earthquakes, floods, droughts, and other disasters unfold in environments where social media platforms rapidly become both information resources and rumor amplifiers. This study developed an applied model for the use of artificial intelligence (AI) in social media for natural crisis management in Iran, with particular attention to detecting, analyzing, and countering misinformation. The article is based on the findings of a mixed-method, exploratory-sequential study conducted in five consecutive phases. First, semi-structured interviews with 16 experts in crisis management, media, artificial intelligence, and relief operations were analyzed thematically, producing nine major themes and ten key factors; instrument validity was confirmed through CVR and CVI, and reliability was supported by Cohen's kappa of 0.847. Second, interpretive structural modeling (ISM) with an eight-member expert panel identified an integrated crisis data monitoring center and transparent privacy legislation as foundational drivers, while reducing response time emerged as the ultimate system outcome. Third, a quantitative analysis of 384 social media messages from the Varzaghan-Ahar earthquake, the Khoy earthquake, and the Lorestan flood showed that 16.9% of messages contained misinformation and more than 45% of rumors appeared during the first six hours of crisis. Fourth, six AI algorithms were evaluated; ParsBERT performed best for Persian text with 88.2% test-set accuracy and an F1 score of 85.7%, CNN achieved 90.0% accuracy in the reported image test subset, and RNN-LSTM reached 83.3% accuracy in the reported video test subset. Finally, the findings were integrated into a five-layer operational model consisting of data, preprocessing, intelligent analysis, human verification and feedback, and decision/action layers. The model specifies three major objectives: preventing rumors and misinformation, reducing response time, and strengthening community resilience. The findings indicate that AI-supported social media monitoring can improve crisis communication, but implementation requires technical, ethical, legal, and social safeguards.

Modeling Marketing Strategies for Small and Medium-Sized Enterprises in the Food Industry Using Reinforcement Learning and Natural Language Processing

Jafar Taherzadeh iD , Hassan Vahedi ^* iD , Seyed Hossein Hosseini iD , Mehdi Sanei iD

Artificial intelligence (AI) has become a strategic enabler for redesigning marketing practices, particularly for small and medium-sized enterprises (SMEs) in the food industry, where firms face intense competition, resource constraints, heterogeneous customer preferences, and infrastructural limitations. This study developed a context-specific model for improving marketing strategies in food-sector SMEs by integrating Deep Q-Network (DQN) and DistilBERT algorithms. A mixed-methods design was used. In the qualitative phase, semi-structured interviews were conducted with 12 experts and analyzed through thematic network analysis. In the quantitative phase, a questionnaire developed from 25 organizing themes was administered to 384 managers and specialists. Reliability was acceptable to excellent (Cronbach's alpha = 0.78-0.88), and normality was confirmed using the Kolmogorov-Smirnov test (Sig. = 0.08-0.19). The highest mean scores were found for senior management technology adoption (4.25) and marketing-strategy personalization capability (4.25). DQN achieved accuracy of 0.94, MSE of 0.15, F1-score of 0.92, and mean cumulative reward of 98.5. DistilBERT achieved accuracy of 0.91, cross-entropy loss of 0.12, precision of 0.89, and recall of 0.90. The findings suggest that DQN is better suited to dynamic marketing optimization, whereas DistilBERT is more appropriate for text-based customer analytics. The proposed framework provides a practical AI-driven model tailored to Iranian food-industry SMEs. The algorithmic analyses were based on structured questionnaire-derived features and coded textual materials from expert and managerial narratives; therefore, the reported performance should be interpreted as internal model validation rather than evidence from deployed real-time marketing campaigns.

AI and Tech in Behavioral and Social Sciences

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Articles

A Comprehensive Model for FinTech Development in Iran's Banking System: A Grounded Theory Approach

Artificial Intelligence and Intelligent Analytical Models in the Evaluation of Audit Report Quality: Evidence from the Tehran Stock Exchange

Explaining and Structuring a Smart Supply Chain Resilience Model in the Face of Global Market Turbulence: A Case Study of the Carpet Industry

The Effect of Generative AI-Based Digital Marketing Communication Personalization on Customer Trust, Customer Experience, and Repurchase Intention: Evidence from Iranian Online Store Customers

Designing a Model of Factors Affecting Online Sales in AI Tool-Based Online Retail Stores

Integrating the SQ6R Strategy into a Personalized Adaptive System for Selected Reading Subskills in Blended EFL Learning

The Role of Managers' Work Experience in the Perceived Effectiveness of Management Accounting Techniques: Evidence from Manufacturing Firms in Toos Industrial Town, Mashhad

The Future of Marketing: Using Agentic AI to Transform Segmentation and Positioning

Knowledge Management, Organizational Health, and Employee Empowerment in Sport and Youth Departments: A Descriptive-Correlational Study

A Conceptual and Theoretical Analysis of Discipline in School Studies: From Educational Order to Power Constructs

The Effect of Managerial Ownership on Corporate Financialization: The Moderating Role of Corporate Technological Innovation

Development and Content Validation of an Expert-Informed Cognitive Performance Intervention Protocol for Children With Specific Learning Disorder: A Qualitative Protocol-Development Study

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Keywords

The Effect of Generative AI-Based Digital Marketing Communication Personalization on Customer Trust, Customer Experience, and Repurchase Intention: Evidence from Iranian Online Store Customers

Intelligent Design and Validation of an Administrative Automation Evaluation Model in Government Organizations: An Exploratory Mixed-Methods Study

Designing a Paradigmatic Model for Boxing Development in Iraq: A Grounded Theory Approach and Data-Based Analysis

A Structural Model of Classic Film Noir Within the SLP Framework: A Systematic Content Analysis of the Interaction Among Visual, Narrative, and Character Structures

Challenges in Harmonizing AI-Enabled Smart Contracts with the General Principles of Contract Law in the Iranian Legal System: A Digital Governance Perspective

Designing a Model for the Development of Sport for All Through Modern Technologies in Iraq

An Applied Artificial Intelligence Model for Social Media-Based Natural Crisis Management in Iran

Modeling Marketing Strategies for Small and Medium-Sized Enterprises in the Food Industry Using Reinforcement Learning and Natural Language Processing