Digital Transformation and Administration Innovation

The aim of this study was to identify the dimensions and components of green human resource management (GHRM) in line with sustainable development within the municipalities of Hormozgan Province. The research employed a qualitative methodology. Participants included mayors, managers, and human resource experts who were selected through purposive sampling and based on the principle of theoretical saturation. The data collection tool was a semi-structured interview, and the validity and reliability of the interviews were ensured using the criteria of credibility, transferability, dependability, and confirmability. The data analysis results revealed that the dimensions and components of green human resource management include the following: training and development systems, human resource retention systems, formulation of a green management charter, green performance evaluation, green organizational citizenship, green human capital, green reward management, green-centric ideation, green innovation, green social culture, green planning, green standards, sustainable green relations, green energy conservation, green environmental research, green organizational culture, green health and safety management, green socialization, green resource procurement, green organizational support, and environmentally-centered disciplinary principles.

The primary objective of this study is to examine modern applications of artificial intelligence (AI) in construction project management and to explore strategies for improving various processes within this industry. Given the increasing challenges and complexities in the construction sector, the introduction and evaluation of AI tools can contribute to enhancing project efficiency, reducing costs, improving forecasting accuracy, and accelerating construction processes. This research investigates these aspects and provides suggestions for optimizing project management processes through the utilization of these advanced technologies. The findings of this study can assist entities active in the construction industry in improving their project management procedures by leveraging AI-based tools, thereby completing projects with higher quality and reduced costs. The present study adopts a quantitative research design and is conducted as a descriptive and survey-based study. Data collection was carried out through library research and the compilation of information from documented and scientific sources. The statistical population of the study includes 384 project managers engaged in the residential construction sector. Data analysis was performed using SPSS version 22. The results of the study indicate that AI tools significantly contribute to improving resource allocation in construction projects. Moreover, the study highlights existing challenges related to the adoption of AI technologies within the construction industry, emphasizing the need for focused attention and resolution of these issues.

The primary objective of this study was to analyze various dimensions of employing artificial intelligence (AI) to enhance customer experience in online retail stores and to identify the factors influencing its success. This research was conducted using a quantitative, descriptive-survey method, and the relationships among AI, service personalization, and customer experience in online retail were examined through structural equation modeling (PLS-SEM). A purposive sampling method was applied to a group of 400 individuals, and data were collected using a researcher-made questionnaire. The validity and reliability of the instrument were confirmed through statistical analyses, and data analysis was performed using SmartPLS 4 software. The mediating role of personalization was assessed using the bootstrap method, and model fit was evaluated using standard indices The key findings of this study demonstrated that the use of AI has a positive impact on both customer experience and service personalization. The effect of AI usage on customer experience was confirmed with a path coefficient of 0.31 and a significance level of 0.001, while its effect on service personalization was confirmed with a path coefficient of 0.54 and a significance level of 0.002. Furthermore, service personalization showed a positive and significant effect on customer experience. In examining mediation, it was found that service personalization plays a partial mediating role in the relationship between AI usage and customer experience. Model fit indices including SRMR, NFI, and GOF indicated a good fit and the model’s capacity to explain the relationships among the variables. The findings of this research revealed that the integration of AI in online retail stores significantly enhances customer experience. This technology, by strengthening service personalization, directly and indirectly influences customer satisfaction. Additionally, the structural equation modeling analysis confirmed that the proposed conceptual model fits well and effectively identifies the complex relationships among the variables.

In today's world, payment companies face security threats, regulatory changes, economic crises, and technological developments that can significantly impact their performance and sustainability. The aim of this study is to analyze the role of Business Continuity Management (BCM) systems in mitigating these risks and enhancing operational and economic resilience. The research method is descriptive-survey, and data were collected through questionnaires designed for managers and senior experts in payment companies. The results indicate that BCM systems are effectively influential in reducing both operational and economic risks. Moreover, these systems contribute to improved economic performance in payment companies by enhancing crisis management processes and reducing the costs associated with disruptions. This study underscores the importance of implementing BCM systems in payment companies and recommends their continuous updating to remain effective against emerging threats.

This study aims to explore the relationship between artificial intelligence and customer experience enhancement in the online retail industry from the perspective of industry professionals. A qualitative research design was employed, utilizing semi-structured interviews with 27 participants working in AI, e-commerce strategy, and customer service roles in Tehran-based online retail businesses. Data collection continued until theoretical saturation was reached. The interviews were analyzed using NVivo software through a three-stage coding process: open coding, axial coding, and selective coding. This approach enabled the identification of key categories and themes that reflect the role of AI in shaping online customer experiences. The analysis revealed six main thematic categories: AI-enabled personalization, service efficiency and responsiveness, human-AI interaction challenges, trust and ethical concerns, technical capabilities and limitations, and customer engagement. Participants emphasized the benefits of personalization and automation, but also highlighted challenges including emotional disconnect, data privacy concerns, and inconsistent AI performance. The study found that while AI improves efficiency and customer satisfaction, its effectiveness is contingent upon ethical design, cultural adaptation, and hybrid human-AI collaboration. AI plays a critical role in enhancing customer experience in online retail, but its full potential can only be realized when technical sophistication is balanced with ethical transparency, emotional intelligence, and user-centered design.

This systematic review article examines the role of competitive intelligence and machine learning in the insurance industry. Given the rapid advancements in information technology and the increasing need for accurate strategic decision-making in the face of intensifying competition, competitive intelligence has emerged as a vital tool for data analysis and predicting competitor behavior in this sector. This study analyzes various methods of applying machine learning to process both structured and unstructured data within the insurance industry and explores the associated challenges and opportunities. The article first analyzes the fundamental concepts of competitive intelligence and developments in information technology in this domain. It then investigates the challenges and requirements of the insurance industry in utilizing advanced data analytics techniques, including machine learning. The findings indicate that integrating machine learning with competitive intelligence can assist insurance companies in conducting large-scale data analysis, thereby enabling more accurate predictions of competitor behavior and market trends. Moreover, the article identifies existing research gaps and presents a conceptual model for the application of competitive intelligence in the insurance industry, offering recommendations for future research and practical applications for insurance industry managers.

Sustainable human resource management is one of the most recent paradigms in human resource management that has received attention over the past two decades. The present study aimed to design a sustainable human resource management model for faculty members of Iraqi universities. This research was qualitative in nature and based on the interpretive paradigm, implemented using the systematic grounded theory approach. Through purposive sampling, 10 Iraqi experts were selected and interviewed in-depth. The resulting data were analyzed through three stages of open, axial, and selective coding, leading to the extraction of 192 final open codes. The open codes were categorized into 48 subcategories and 20 main categories. The study's paradigmatic model was designed with the following sections: contextual factors (global sustainability contexts, national sustainability contexts, and higher education sector-specific contexts); causal conditions (increasing public awareness and concern, and the importance of sustainability in achieving competitive advantage); intervening conditions (organizational culture and leadership, and availability of sufficient resources); core phenomenon or sustainable human resource management practices in universities (including sustainable recruitment and hiring, sustainability training and training in a sustainable manner, sustainable performance management, sustainable rewards and benefits, and provision of appropriate work arrangements); strategies (aligning university goals with sustainable development goals, developing a comprehensive university sustainable human resource strategy, implementing university sustainable human resource strategies and policies, overcoming individual resistance to change, promoting research in the field of sustainability, engaging stakeholders, integrating sustainability into university curricula, and sustainability performance reporting); and consequences (enhanced sustainable performance, improved status of faculty members, enhanced university reputation, and facilitation of sustainable community development).

The present study was conducted with the aim of designing a value chain management model for the optimal fulfillment of social responsibility in the context of blockchain technology. The research method employed was qualitative, based on the grounded theory strategy. For data collection, the tool of in-depth interviews was used. The target population consisted of academic experts, managers, and senior specialists who were involved in the processes and decision-making of value chain management in the insurance industry. Through purposive sampling and deep interviews, theoretical saturation was achieved. In the grounded theory approach used in this study, through three stages of open, axial, and selective coding, the general categories were represented within a paradigmatic framework, including causal conditions, contextual conditions, intervening conditions, core categories, strategies, and outcomes, all based on value orientation. The results indicated that all relationships among variables were significant. Specifically, the following influential relationships were confirmed: causal conditions had a significant effect on the main category of the model; contextual conditions influenced the strategies; intervening conditions also had a significant effect on the strategies; the main category had a direct impact on the strategies; and finally, the strategies had a direct and significant effect on the research outcomes. In the Importance-Performance Map Analysis (IPMA) section, the model’s indicators were evaluated in terms of importance and performance. These results can serve as a basis for strategic and managerial decision-making in various fields, especially in the evaluation and improvement of organizational processes and change management strategies. In summary, this study effectively evaluated the conceptual model and demonstrated that various factors such as causal conditions, contextual conditions, intervening conditions, and strategies influence the research outcomes. Moreover, the structural model of the study exhibited high goodness-of-fit and validity, and the relationships among its variables were confirmed.