Modern Research in Decision Making

Abstract Export development has always been regarded as one of the key macroeconomic strategies for countries to overcome economic crises and achieve sustainable growth. Classical economic theories, neoclassical theories, and new growth theories emphasize the simultaneous role of exports and technology as engines of economic growth. In previous research, knowledge-based export development issues have often been examined at three levels—governance, organizational, and marketing—primarily from a macro perspective and with policy-making objectives.
This study aims to provide a practical process model for the export development of Iranian knowledge-based companies through firm internationalization, identifying marketing strategies for their export expansion and presenting an Interpretive Structural Model (ISM) of these strategies. The research adopts a sequential mixed-methods approach. In the first stage, key strategies were extracted through thematic analysis of semi-structured interviews with export managers of knowledge-based companies. In the second stage, using the Interpretive Structural Modeling (ISM) method and the participation of academic experts and professionals in the knowledge-based sector, a three-stage model was developed, comprising market learning, market building, and market leadership. This model, consisting of 15 strategies, outlines an operational pathway for knowledge-based export development.
Moving beyond merely discussing barriers and drivers, this study focuses on the marketing dimension with a novel approach, presenting a multi-stage operational model that can serve as a practical roadmap for knowledge-based export companies at different internationalization levels. Additionally, the research findings can serve as a framework for policymakers at the governance level and managers of knowledge-based companies to strengthen their export position in global markets.

Abstract Due to technological development, customer expectations have increased in various dimensions. In the service systems, customers value customer experience, convenience, and speed of service delivery. Utilizing Industry 4.0 technologies is one of the main strategies facing banks for survival and growth in such an environment. A technology roadmap is a tool that helps decision-makers identify and evaluate different options to achieve technological goals. In this article, after reviewing the background, six Industry 4.0 technologies were identified in the field of banking, including omnichannel banking, branchless banking, blockchain-based banking, social media banking, open banking, and robotics in banking. The value of each technology was calculated in two-year time intervals based on financial-economic, performance, and user experience criteria, using a combination of the technology development envelope model and the best-worst method. Then a ten-year technology roadmap was created for the Iranian banking industry. The findings show that although social media banking currently seems to be the most attractive technology for the Iranian banking industry, in the coming years, omnichannel banking will be the dominant technology in this industry in terms of attractiveness and value creation.

Abstract In modern business, organizations require data-driven marketing analytics, big data capabilities, and artificial intelligence (AI) to make effective decisions. This study simultaneously examines these three elements and evaluates the role of AI as both a mediator and moderator in enhancing marketing analytics and strengthening corporate marketing capabilities. The volume of data generated across multiple channels, along with the complexity of consumer behavior, poses significant challenges for extracting actionable insights for effective marketing strategies. This research is applied in nature. The study population included industry and academic experts from Iran and Canada. Using Cohen’s formula for an undefined population, the sample size was determined as 138 participants and selected through a non-probabilistic convenience sampling method. Data were collected via a questionnaire, with reliability confirmed for all items (Cronbach’s alpha = 0.778). Findings indicate that big data capabilities significantly impact data-driven marketing analytics, while AI functions as both a mediator and moderator in the relationships among variables. Furthermore, results suggest that the development of corporate marketing capabilities through marketing analytics can be achieved with higher accuracy and speed. The main contribution of this study lies in examining the simultaneous mediating and moderating roles of AI in strengthening the relationship between big data capabilities and the effectiveness of data-driven marketing analytics.

Abstract With the rapid growth of online shopping and the significant role of social media in consumer decision-making, purchase intention prediction has become one of the key issues in digital marketing. Accurate prediction of this behavior can improve advertising targeting, increase customer conversion rates, and optimize sales strategies. The purpose of this study is to propose a hybrid machine learning model for predicting users’ purchase intentions based on their responses to advertisements. The dataset consists of 1,000 records and 10 relevant features (including age, gender, income, time spent on the website, daily internet usage, advertisement title, location and time of interaction, and click label), which were collected and cleaned from public sources (Kaggle). After preprocessing and feature selection, a stacking approach was employed, where the outputs of base models (logistic regression and support vector machine) were combined and used for final prediction. Model evaluation using accuracy, precision, recall, and F1-score demonstrated that the proposed model achieved strong performance (accuracy = 0.96, precision = 0.98). The findings indicate that combining demographic and behavioral features can improve purchase intention prediction and provide practical implications for targeted advertising in e-commerce platforms.

Abstract In the face of growing complexities and continuous instabilities across supply chains, intelligent decision-making and enhanced organizational resilience have become indispensable. The purpose of this study is to develop an intelligent framework for strengthening supply chain resilience by leveraging emerging technologies, particularly artificial intelligence and blockchain. This research adopts a mixed-method approach. In the first phase, a systematic literature review was conducted on 63 studies published between 2015 and mid-2025 in Scopus and Web of Science databases, from which 34 relevant papers were selected after screening. Subsequently, thematic analysis was performed using MAXQDA 2022 to extract key components and develop the initial conceptual framework. To localize and validate the findings, semi-structured interviews were conducted with 20 experts in the field of smart supply chains, and the results were re-analyzed through thematic analysis. In the quantitative phase, the fuzzy Delphi method was employed for criteria screening, while the fuzzy DEMATEL technique and fuzzy ANP were utilized to determine causal relationships, influence levels, and criteria weights. The findings reveal that emerging technologies significantly enhance supply chain resilience by improving information transparency, accelerating decision-making, and strengthening predictive capabilities. “Supply chain digitalization,” “smart agricultural economy,” and “institutional policymaking” were identified as the key drivers, whereas “technological collaboration” and “process automation” exerted the greatest influence on system adaptability and recoverability. Overall, the results provide an intelligent roadmap for decision-making under uncertainty and for developing technological capacities within supply chains.

Abstract This paper aims to provide a practical and comprehensive guide for selecting appropriate statistical methods in social science and management research by systematically examining various statistical techniques and their applications in data analysis. Given the increasing complexity of relationships among variables and the significant diversity of measurement scales in human sciences studies, the selection of a suitable statistical method is of paramount importance and plays a decisive role in the validity and precision of research findings. To this end, this article presents comprehensive and applied flowcharts and tables to assist researchers in choosing a proportionate and efficient method by carefully considering the type and number of variables, research objectives, and the assumptions of statistical methods. The primary goal of these tables is to facilitate the decision-making process for researchers. Furthermore, the present paper discusses the role and importance of statistical methods in enhancing the quality and credibility of social science and management research, demonstrating that the correct choice of statistical techniques can lead to increased accuracy, generalizability, and validity of research outcomes. Ultimately, this article endeavors to aid researchers in the fields of social sciences and management in conducting more precise and credible research by offering practical guidance. In this regard, the paper makes the practical application of statistical methods more tangible for researchers by including concrete examples from various managerial domains.