Management Research in Iran

Abstract Humans are inherently altruistic beings. This innate altruism leads them to forego self-interest for others and engage in charitable giving. Today, as charity and NGO,s are facing a shortages in government support and financial resources, they have been inteseted in using cost-effective marketing methods. This research investigates one of the most affordable charity marketing methods: Fundraising Letters and Campaigns. The study aims to help charities attract more donations by determining the framework of Fundraising texts and their effective elements.
The mixed-methods study conducted in qualitative and quantitative phases. Initially, 26 campaigns from 8 charity websites and 18 letters from 5 charity organizations were examined using Genre Analysis. In the quantitative phase, two charity texts were studied by subjects. Ultimately, 7 moves and 21 steps were identified in the letters, and 8 moves and 17 steps were identified in the charity campaigns.
Furthermore, eye-tracking results revealed that the "Soliciting help " move in charity letters and the "introducing charity" move in campaigns have been indentified as Pont of Gaze; thus they are the most important moves in each text. According to the results, the middle paragraph of charity fundraising texts receive more attention than other parts; therefore, it is recommended that important elements be placed in the middle paragraphs of fundraising texts.

Abstract Considering the many challenges that petrochemical industries are facing today, including new technologies, sanctions and production issues, strategic planning has become one of the key tools to strengthen these industries in the field of national, regional and international competition. The competence and ability of senior managers in designing and implementing strategic plans plays a vital role in the success of organizations. This research was conducted with the aim of identifying the strategic competencies of senior managers and employees of Nouri Petrochemical Company in order to achieve strategic goals in the petrochemical industry. By using the theme analysis method, the aforementioned competencies were identified in five dimensions, including knowledge, skills, experience, attitude and personality traits, and categorized into 10 main components and 53 sub-components. Then these components were evaluated using five-point Likert scale. The results of the research showed that it was ranked 5 in many of these components, which is a confirmation of the successes of Nouri Petrochemical Company and its capable strategic management. Hence, this company can serve as a model for other petrochemical complexes in Iran and even the world. Also, using the experience and knowledge of these capable managers in the form of training sessions can help increase productivity in other petrochemical complexes of the country

Abstract This research, with the aim of providing more accurate and effective solutions, has used the Delphi method based on artificial intelligence and expert opinions to identify and prioritize the needs and personal development skills of human resource managers. This research is applied in terms of its purpose, which seeks to combine the human-based Delphi method (traditional) and artificial intelligence (modern), to provide an innovative approach in the field of research methods and data analysis that can be classified as fundamental research. The statistical population of the research is 15 experts in the fields of human resources, coaching and personal development and three types of language models such as Microsoft Copilot, Chat GPT and Gemini. The data collection tool in this research was the Delphi method, and the angle method was also used to examine the validity of the data. The findings showed that the Delphi method based on artificial intelligence analyzes the components with high speed and accuracy and provides access to rotational data. This method consists of 5 stages that begin with precise prompt writing. This method is best used as a complementary method along with human methods.

Abstract This study examines the impact of the most prominent early warning signs and causes of out-of-sequence (OOS) work on the productivity of industrial construction projects. Relying on the opinions of 16 industrial construction professionals and using the fuzzy Delphi technique, the most relevant of these signs and causes were identified. Then, using Max-Max and Bayes rules, the impact of this signs and causes on the productivity of industrial construction projects was analyzed. The findings indicated a high level of agreement between the rankings of causes of OOS work and a moderate level of agreement between the rankings of early warning signs, as assessed using both the Bayes and Max-Max approaches. Combining the findings of the two approaches revealed that if any of the following six early warning signs are observed—regardless of which sign precedes the occurrence of any cause of OOS work—it can be expected to lead to substantial decreases in project productivity: increase in drawing revisions, Inexperience in key roles, trending away from baseline progress curve, inadequate transition planning from construction to commissioning, high percentage of rework, and float usage early in schedule. Additionally, among the most obvious causes of OOS work, lack of consideration of stakeholder requirements in project planning, change in design, poor communication between different project parties throughout the project, late delivery from vendors, unrealistic activities duration, and lack of access to full project, respectively, can cause the greatest damage to the productivity of construction projects.

Abstract Recently, with the penetration of artificial intelligence, the marketing environment of small and medium enterprises has experienced significant developments. The aim of this research is to identify the most influential contributors, current trends, and future research directions in the field of artificial intelligence in digital marketing in small and medium enterprises. For this purpose, bibliometric data of 100 documents between 2000 and 2025 in the field of artificial intelligence in marketing research were extracted from the Scopus database. VOSviewer version 1.6.20.0 software was used for the analysis. The results and publication trends indicate the annual growth in artificial intelligence in marketing research. In addition, keyword co-occurrence analysis uncovered four main thematic clusters: Cluster 1 - Digitalization of SMEs, Cluster 2 - Marketing Opportunities of AI in Industry, Cluster 3 - Data Analytics and Advanced Decision Making, and Cluster 4 - Marketing Strategies and Social Media. By identifying current thematic clusters, this study identifies future research areas. It also has practical implications for marketers in SMEs and shows how AI can be used to improve business operations.

Abstract Digital transformation, as one of the most important drivers of change in contemporary banking, has had profound impacts on banking structures, processes, and services. This paper aims to identify research areas and trends in digital transformation in banking, and conducts a comprehensive bibliometric analysis based on bibliographic data available in prominent academic databases. Using the VOSviewer tool, scientific mapping and visualization of relationships between keywords, authors, and publications are performed, and major clusters in the literature in this field are identified. The findings show that digital transformation research in banking is focused on areas such as artificial intelligence, cybersecurity, fintech, open banking, and customer experience. Emerging trends in digital banking, including the use of new technologies such as blockchain and cloud computing, are also examined. By analyzing patterns and research gaps, this paper suggests future directions for academic studies in this area and emphasizes the need to develop innovative strategies for banks to adapt to the challenges and opportunities of digital transformation. The results can help researchers, policymakers, and bank managers to better understand key trends and design data-driven solutions for the future of banking.