New Approaches in Management and Marketing

Abstract The aim of this study is to provide a framework for implementing AI‑based demand‑driven business intelligence in the apparel industry. In terms of purpose, this research is applicable; and in terms of methodology it follows a mixed‑methods approach (qualitative and quantitative).
The statistical population in the qualitative phase consisted of 17 participants, including university professors, experts, sales specialists, managers in the apparel industry, and artificial intelligence specialists, selected through purposive sampling. In the quantitative phase, the statistical population consisted of 384 individuals, including managers, senior experts, data analysts, supply chain managers, and information technology specialists in the apparel industry, selected by cluster random sampling. Data collection instruments included semi‑structured interviews and questionnaires. For data analysis, MAXQDA 2020 software was employed in the qualitative phase, while SPSS and PLS were employed in the quantitative phase. The results of data coding showed that 675 initial codes were refined to 461 cleaned codes and subsequently categorized into 214 conceptual codes, 45 sub‑categories, 19 main categories, and 6 core categories.
Based on the research paradigm model, causal factors include increasing competition, changes in customer roles and market behavior, rising internal costs, and resource constraints. Continuous learning and monitoring, as well as privacy protection, serve as positive interventions in this process, whereas factors such as lack of artificial intelligence skills, resistance to change, and legal limitations may act as inhibiting interventions. These factors influence strategies such as transformational leadership, organizational memory management, and technological investment. The final model is aligned with the internal and structural challenges faced by businesses.
Introduction
Today, in some market segments, the speed of production cycles has become more important than cost, because shorter production times provide the flexibility required to adapt to rapidly changing fashion trends in these market segments. Accordingly, selecting the most beneficial supply chain strategy has become an established research topic, particularly for fashion products where demand for greater variety exists within short time intervals. At the same time, managing demand and supply simultaneously is not easy, as supply chains have become increasingly complex due to outsourcing processes (Anitha & Neelakandan, 2024).
The apparel industry, characterized by short product life cycles and rapid changes in customer preferences, requires highly flexible supply chains. Fast fashion brands, due to their need for rapid responses to fashion changes, require responsive and flexible supply chains, whereas traditional brands focus more on efficiency and productivity through economies of scale. The sensitivity of retail brands to fashion trends shapes their supply chain strategies. Fast fashion brands require high flexibility and speed in their supply chains in order to respond to market demand in a timely manner. Coordination between demand and supply remains challenging, and achieving mutual benefits for both manufacturers and retailers require precise supply chain management (Swaminathan & Venkitasubramony, 2024).
Choosing between these two strategies requires consideration of consumer purchasing behavior as well as competitors’ strategies. The interaction of these factors leads to complex market structures that require appropriate tools for evaluating strategies. Agent‑based modeling and simulation enable the consideration of individual consumer preferences, social interactions, and competition among producers, thereby illustrating the overall behavior of the system (market) at a macro level (Rand et al., 2018).
This research not only seeks to present a theoretical model but also aims to assist manufacturers and businesses active in the apparel industry in moving beyond traditional approaches toward production aligned with real demand and value creation. Such a model, while preserving cultural standards, can contribute to sustainable growth, social identity formation, and economic development. Considering the above, the present study seeks to answer the main research question: How can a framework for implementing AI‑based demand‑driven business intelligence in the apparel industry be developed?
Theoretical Framework
Demand‑Driven Business Intelligence
Demand‑driven business intelligence refers to an approach to business intelligence that focuses on analyzing and understanding customer needs, preferences, and market behaviors in real time or near real time. This type of business intelligence utilizes both internal and external data to help organizations base their decisions on actual market demand rather than relying solely on historical data (Ben et al., 2019).
Artificial Intelligence
Artificial intelligence (AI) is considered a strategic approach for maximizing the use of technology and market data in order to improve customer experience. Based on previous studies, advanced AI techniques include tools such as email automation, chatbots, and various analytical systems that assist managers in introducing product features to customers on a broad scale without temporal or spatial limitations, thereby creating a better purchasing and consumption experience for customers (Hayatmehr et al., 2026).
Hayatmehr et al. (2026) examined the impact of artificial intelligence applications and intelligent learning on the strategic thinking skills and academic performance of management students, considering the moderating role of individual ethics. The results indicated that the use of AI tools has a positive effect on strategic thinking dimensions (systems thinking, creative thinking, future-oriented thinking, and critical thinking), intelligent learning, and academic performance. The mediating role of strategic thinking—particularly critical thinking and future-oriented thinking—was confirmed in the relationship between AI usage and academic performance, as well as between intelligent learning and academic performance. Additionally, individual ethics not only had a positive effect on intelligent learning but also played a moderating role in the relationship between intelligent learning and strategic thinking (systems thinking, critical thinking, and future-oriented thinking).
Zolghadr et al. (2026) investigated the modeling and validation of the role of artificial intelligence in enhancing the export capabilities of companies in the electronics industry using a mixed‑methods approach. The qualitative results indicated that the model consists of five main categories: causal conditions (ICT infrastructure, data quality, and technical capacity), contextual conditions (supportive policies, international collaborations, and an innovative organizational culture), intervening conditions (sanctions, rapid technological changes, and legal and customs regulatory barriers), strategies (demand forecasting, price optimization, logistics intelligence, and human resource empowerment), and outcomes (competitive advantage, penetration into global markets, increased customer satisfaction, and cost reduction). In the quantitative phase, composite reliability indices were all above 0.7 and convergent validity for most constructs exceeded 0.5. The hypothesis testing results also confirmed all relationships among the model constructs with a significance level of p < 0.001.
Research Methodology
This study is applicable in terms of purpose, and adopts a mixed‑methods approach (qualitative and quantitative) in terms of research design.
In the qualitative phase, the statistical population consisted of 17 university professors, experts, sales specialists, managers in the apparel industry, and artificial intelligence specialists selected through purposive sampling.
In the quantitative phase, the statistical population included 384 individuals consisting of managers, senior experts, data analysts, supply chain managers, and information technology specialists in the apparel industry. These participants were selected by cluster random sampling.
The data collection tools consisted of semi‑structured interviews and a questionnaire.
Research Findings
For data analysis, MAXQDA 2020 was applied in the qualitative phase, while SPSS and PLS were applied in the quantitative phase. The results of the coding process reduced the initial 675 codes to 461 refined codes, ultimately categorized into 214 conceptual codes, 45 sub‑categories, 19 main categories, and 6 core categories.
Based on the study’s paradigmatic model, the causal factors include increased competition, changes in the roles and behaviors of customers and markets, rising internal costs, and resource constraints. Continuous learning and monitoring, as well as the protection of privacy, are considered positive interventions in this process. In contrast, factors such as the lack of AI skills, resistance to change, and legal limitations can act as inhibiting interventions.
These factors influence strategies such as transformational leadership, organizational memory management, and technological investment. The final model is aligned with the internal and structural challenges faced by businesses.
Conclusion
The present study was conducted with the aim of developing a framework for implementing AI‑based demand‑driven business intelligence in the apparel industry. The findings of this research are consistent with the results of previous studies (Hayatmehr et al., 2026; Zolghadr et al., 2026; Ghanbari Gheshlaghi et al., 2025; Liu et al., 2025; Swaminathan & Venkitasubramony, 2024; Anitha & Neelakandan, 2024; Roth, 2024; Hemati, 2024; Kunz et al., 2023; Hajipourfard et al., 2022; Wamba‑Taguimdje et al., 2020).
Swaminathan and Venkitasubramony (2024), through a systematic review and comprehensive analysis of reputable studies, compared statistical methods, machine learning, and deep learning approaches, identifying fundamental challenges in the fashion industry such as short product life cycles, high variety, and demand volatility. Their findings demonstrated that AI models provide higher predictive accuracy in this industry and emphasized the growing necessity of robust validation frameworks within demand‑driven models.
Based on the research results, it is recommended that apparel industries develop the necessary foundation for activating AI‑based demand‑driven business intelligence by establishing core infrastructure, gradually developing knowledge and human capital, redesigning decision‑making structures, and institutionalizing a data‑driven culture. Such a foundation is neither inherently complex nor excessively costly; rather, it can be implemented gradually and aligned with the actual capacities of the national apparel industry. Acknowledgments The authors gratefully acknowledge all individuals, institutions, and organizations that provided scientific, technical, administrative, or advisory support throughout this research and the preparation of this manuscript. Conflicts of Interest The authors declare that they have no financial or non-financial, personal, professional, or institutional conflicts of interest relevant to this work. Data Availability Statement The data supporting the findings of this study are available from the corresponding author upon reasonable request. AI Use Statement The authors declare that no generative artificial intelligence (AI) or AI-assisted technologies were used in the preparation, writing, or editing of this manuscript. Funding The authors declare that no funds, grants, or other financial support were received during the conduct of this research or the preparation of this manuscript. Ethics Approval All procedures performed in this study were conducted in accordance with the ethical standards of the responsible institutional and national research committee.

Abstract The aim of the present study is to investigate the effect of user capacities and live shopping interaction on social media on purchase intention and actual purchase behavior among Instagram users. This study is applied in terms of purpose and descriptive-analytical in terms of method, which was conducted using a survey method and a causal approach. The statistical population of the study is active users on Instagram. Due to the unlimited number of users, the statistical sample is 361 people, who were selected using the Cochran formula and a simple random method. The primary data collection tool is a standard questionnaire, the validity of which was confirmed by factor analysis with the KMO index and their reliability by reporting the Cronbach's alpha coefficient. SPSS software version 24 and LISREL 8.8 software were used in data analysis. The findings show that user capacities, including visual capacity, shopping guidance capacity, and active presence capacity, have a significant effect on live shopping interaction on Instagram. This indicates the key role of user characteristics and capabilities in the formation of effective interactions in the live shopping context. Also, live shopping interaction on Instagram has a positive and significant effect on users' purchase intention. This finding shows that increasing the level of user interaction in the live shopping process can lead to strengthening their willingness to buy and play an important role in purchasing decisions. The results of the study also show that purchase intention has a direct effect on users' actual purchase behavior. This relationship indicates that the formation of purchase intention, as a mediating variable, plays a decisive role in converting online interactions into actual purchase behavior.

Abstract The present study was conducted based on the paradigm of pragmatism and with a combined approach of induction and deduction. To achieve the study's objectives, a qualitative research design was used. This research is of an applicable-developmental type and aims to present a paradigmatic model for improving customers' mental image of Tehran chain stores. In terms of data collection, the research is classified as a non-experimental (descriptive) study conducted with a cross-sectional survey approach. The participants were managers of chain stores, and sampling was carried out using a theoretical method; meaning that individuals were selected based on their information adequacy and capacity to enrich the categories, and theoretical saturation was achieved by conducting 21 interviews. Data were collected through semi-structured interviews and a questionnaire. The indicators affecting customers' mental image of the store were extracted using the data-driven theory method and MaxQDA24 software and then prioritized using the fuzzy Savaray method. The results of the research model showed that causal conditions (customer experience management, co-creation of value with the customer, customer orientation and service quality) affect the central phenomenon (customers' mental image of the store). The mental image of the store, along with contextual conditions (marketing mix) and intervening conditions (store social responsibility), affects strategies and actions (internal branding and branding strategy). Ultimately, the aforementioned strategies lead to outcomes such as customer loyalty, satisfaction and trust, which reflect the dynamics and multidimensional function of mental image in promoting long-term customer relationships with the store.
Introduction
In recent years, chain stores with various brands have grown significantly in the country, and a fierce competitive arena has been created to attract and retain customers (Torkanloo et al., 2025). In such circumstances, customers' purchasing behavior is more influenced than ever by their perceptions and mental images of the stores. Therefore, branding and efforts to improve the store brand are a decisive factor in success (Solati et al., 2024). Sales managers and chain store owners have well understood that improving the store's mental image in the minds of customers can pave the way for improving the overall brand image and increasing the willingness to buy. By improving the store's image in the minds of customers, the likelihood of customer retention and return will increase (Sudirman & Muis, 2025). In this regard, understanding customers' mental perceptions and understanding how their attitudes towards the store are formed is considered a major prerequisite for developing effective marketing strategies and customer experience. Continuing to be present in such a dynamic market requires targeted investment in branding and creating differentiation in brand identity (Yuan & Yang, 2024). On the other hand, consumers' needs, preferences, and decision-making patterns have also undergone profound changes and have reached a more complex level than in the past. In response to these changes, large chain stores are trying to satisfy a diverse range of customers by offering a wide range of goods and brands (Jiang & Wu, 2023). However, this product variety alone does not guarantee success; because the main focus should be shifted from the brand of the goods to the store brand and the image that the customer has of the entire shopping experience. Therefore, in the current competitive conditions, retailers must improve their image among customers in order to defend their position in the market (Nakamori et al., 2024). In Iran, with the emergence of new structures and the use of new technologies in the retail sector, a fierce competitive environment has been formed between stores to attract the attention and trust of customers. Customers in this market, based on their mental impressions and associations, have a specific idea about each store, which creates a mental image of the store within their perception (Berah moghadam et al., 2024). According to this view, consumers keep a set of real or mental beliefs about each store in their memory and are confident in the accuracy of those beliefs. In such circumstances and given the increasing competition, it is necessary for the country's chain stores to adopt a customer-centric approach to marketing in order to recognize their current position and plan to achieve the desired situation (Raispour et al., 2025). Statistics show that customers play a key role in the continuation of competition and profitability of chain stores. Global studies indicate that an increase of only 5% in customer retention rates can increase company profits by 25 to 95% (Jones et al., 2025). Successful branding has caused a change of about 15% in customer retention. Also, consumers who are loyal to brands make up 65% of the company's business and spend 43% more on brands they are loyal to (Stephens et al., 2025). According to statistics from the National Chain Store Association, the turnover of the country's retail industry was estimated at about 920 trillion, of which the share of chain stores Chains accounted for 10 percent, and the rest belonged to small and traditional retailers (Bormeideh et al., 2024). Also, in the retail industry, which also includes chain stores, the average customer retention rate globally has been reported to be about 63 percent, while the overall average for other industries is about 75 percent (Aghakhani Bezdi Langari & Hassani, 2023).
In such a situation, the gap between the actual perception of customers and the marketing approaches of chain stores increases, and the need for a scientific analysis of this gap and a solution to it is highlighted. Accordingly, the aim of the research is to identify the components affecting the mental image of customers, explain the cognitive and behavioral mechanisms that shape this image, and present a native paradigmatic model to improve the perception of customers of chain stores in Tehran. This aim has been formulated in order to answer a fundamental question: How is the pattern of improving the mental image of customers in chain stores in Tehran explained and what elements and relationships shape it?
Theoretical framework
Brand image
Brand image is a multidimensional concept that reflects the perceptions, feelings, and accumulated experiences of the audience of a brand. This image is the result of a continuous process of interaction between the customer and the brand and shows how people mentally perceive the identity, value, and reputation of the brand (Tahir et al,. 2024).
Store Image
The concept of "store image" was first introduced in the marketing management literature in 1958 by Martineau. He considered store image to be a set of customer perceptions formed by a combination of functional characteristics (such as the quality of goods and services, layout and price) and psychological characteristics (such as store atmosphere, employee behavior and customer emotions). Although this concept did not receive much attention in the early years, its importance has increased significantly in the last two decades with the rapid expansion of chain stores and changes in shopping patterns (Lang et al., 2023).
Research Methodology
This study was designed based on the paradigm of pragmatism and with a combined approach of induction and deductive and was conducted with the aim of providing a paradigmatic model for improving customers' mental image of chain stores in Tehran. In terms of methodology, it is classified as non-experimental and descriptive research and was implemented in the form of a qualitative design and cross-sectional survey. In the qualitative section, the participant population included theoretical experts in the field of marketing management and empirical experts including senior managers, marketing managers, and customer experience managers in chain stores in Tehran, and ultimately 21 interviews were used as the basis for analysis.
Research findings
The final result of the analysis was the identification of 6 selective codes, 12 central codes, and 63 open codes. The results of the research model showed that causal conditions (customer experience management, co-creation of value with customers, customer orientation and service quality) affect the central phenomenon (customers' mental image of the store). The mental image of the store, along with contextual conditions (marketing mix) and intervening conditions (store social responsibility), affects strategies and actions (internal branding and branding strategy). Ultimately, the aforementioned strategies lead to outcomes such as customer loyalty, satisfaction and trust, which reflect the dynamics and multidimensional function of the mental image in promoting long-term customer relationships with the store. It was also determined that the perceived risk index of purchasing from the store is in the first priority, the ethics index and adherence to ethical responsibility is in the second priority, the index of providing products and services based on customer needs is in the third priority, the index of establishing the possibility of mutual and reciprocal relationships with customers with a weight of 0.0864 is in the fourth priority, and the index of mental association of the store with customers is in the fifth priority.
Conclusion
The research results are in line with domestic and foreign studies, and in the form of an integrated model, it explains the mechanism of customer image formation. Causal factors including customer experience management, value co-creation, customer orientation and service quality, like the findings of Torkanloo et al. (2025), Yuan & Yang (2024), and Mousavi & Fadai (2023), confirm the determining role of customer experience, interaction and recognition of real customer needs. In the context of factors, the marketing mix and social responsibility are introduced in accordance with foreign research, guiding perception and strengthening positive mindset. Customer image as a multidimensional construct including perceived value, risk, ease of purchase, and brand associations is presented in this study in a more coherent way than previous studies. Internal branding strategies and brand strategy show that employee alignment and clear policy-making play a direct role in shaping image. The final outcomes including trust, satisfaction and loyalty are also consistent with the results of previous research and emphasize that positive image strengthens purchasing behavior and customer relationship continuity.
The suggestions emphasize the importance of creating a lasting experience for customers through dynamic environmental design, targeted sensory stimuli, and professional employee behavior, and further consider it essential to provide a platform for two-way interaction for co-creation of value by utilizing behavioral data and customer participation in improving services. Also, strengthening the customer-centric approach by deeply understanding needs, providing personalized offers, and focusing on key customers is introduced as an effective factor in increasing loyalty. Improving the quality of services by improving physical standards, effective responsiveness, and strengthening empathetic behavior of employees, along with intelligent design of the marketing mix including product, price, promotion, and distribution, can create a coherent and attractive experience. Attention to social responsibility and institutionalization of ethical and environmental principles provide the basis for public trust and strengthening brand credibility, and ultimately focusing on improving the mental image of customers and developing internal and strategic branding strengthens organizational cohesion and a positive perception of the value and quality of Tehran chain stores. Acknowledgments The authors gratefully acknowledge all individuals, institutions, and organizations that provided scientific, technical, administrative, or advisory support throughout this research and the preparation of this manuscript. Conflicts of Interest The authors declare that they have no financial or non-financial, personal, professional, or institutional conflicts of interest relevant to this work. Data Availability Statement The data supporting the findings of this study are available from the corresponding author upon reasonable request. AI Use Statement The authors declare that no generative artificial intelligence (AI) or AI-assisted technologies were used in the preparation, writing, or editing of this manuscript. Funding The authors declare that no funds, grants, or other financial support were received during the conduct of this research or the preparation of this manuscript. Ethics Approval All procedures performed in this study were conducted in accordance with the ethical standards of the responsible institutional and national research committee.

Abstract The aim of the present study is to present a model for the application of artificial intelligence in the export of electronics industry products. The research method is applicable in terms of its purpose, and quantitative in terms of its implementation method. The statistical population of the study included sales experts and marketing specialists in electronics export companies, whose sample size was estimated using the stratified random sampling method and the Cochran formula. Given that the number of these people is about 1600, the sample size was considered to be 306 people. The data collection tool is a researcher-made questionnaire. SPSS and PLS software were used to analyze the data. The results showed that the composite reliability indices were all more than 0.7 and the convergent validity for most categories was more than 0.5. The results of the hypothesis tests also indicate the complete confirmation of the relationships between the model categories with a significance level of p<0.001. Also, the GoF criterion was used to examine the overall fit of the model and the measurement for the structural model. The GOF value is 0.815, indicating a strong fit of the model.
Introduction
In today's advanced world, where the speed of change is doubling every day, domestic performance in relation to business activities is not enough to remain competitive. Rather, it is better to expand the scope of business operations beyond the country's borders. Companies or organizations that are interested and inclined to enter foreign markets must gain a proper understanding of the global trade system (Wang et al., 2023).
Several factors may expand the scope of a company's activities to the international arena. The attack of foreign competitors on a company's domestic and local market by offering better products or prices is one of these factors (Xu & Tian, ​​2023). The company's foreign market may be shrinking or the company may consider it advisable to enter new markets to produce more and take advantage of economies of scale. The company’s goal in entering foreign markets may be to break away from market dependence and to escape the risks of such dependence (Yan, 2023). The process of smartization is one of the characteristics that reduces the challenges facing organizations in enabling them to be present in international markets (Alyan, 2022). Collecting, analyzing, storing, and using data to design better experiences for people is a very difficult task, but with the help of new technological advances such as artificial intelligence and robotics applications, the marketing department of the company can use complex applications for production, distribution, and export, as well as create appropriate customer experiences (Prakash, 2023). By analyzing large amounts of data, artificial intelligence can bridge the gap between data science and implementation, which was previously an impossible task. The advancement of technology use in exports is the result of the integration of big data with the academic study of intelligent systems (Gabelaia, 2022). The Internet of Things, data science, cloud computing, big data, artificial intelligence, and blockchain are all technological innovations that are changing the way we live, work, and even interact with international markets. Further development of these technologies may lead to super automation and large-scale networks that will usher in the Fourth Industrial Revolution (or Industry 4.0) (Saheb et al., 2022). However, few studies have examined the role of artificial intelligence on exports in the electronics industry, and given the existing research gap, the present study answers the question: what is the application model of artificial intelligence in the export of electronics products?
Theoretical Framework
Artificial Intelligence
Artificial intelligence is a branch of computer science with a long history. While in the past, artificial intelligence was limited to a largely theoretical field, recent advances in data generation and computing have allowed artificial intelligence to move from theory to application (Haenlein & Kaplan, 2019).
Application of Artificial Intelligence in Exporting Electronics Products
AI-based quality control systems, by monitoring the production and distribution process in real time, ensure the quality level of products before they are shipped to international markets and prevent product returns or brand losses (Barykin, 2023). In the electronics industry, where competitive advantage is based on innovation, speed of response, and supply chain optimization, AI acts as a strategic driver for improving export performance indicators. Companies that apply AI technologies in their marketing, demand planning, and export logistics processes have more sustainable growth and higher market share compared to competitors (Zhai, 2022).
Zolghadr et al. (2026) investigated the modeling and validation of the role of artificial intelligence in enhancing the export capabilities of electronics companies: a mixed approach. The results of the qualitative section showed that five main categories including causal conditions (ICT infrastructure, data quality, technical capacity), contextual conditions (supportive policies, international cooperation, innovative organizational culture), intervening conditions (sanctions, rapid technological changes, legal barriers and customs regulations), strategies (demand forecasting, price optimization, logistics intelligence, human resource empowerment) and consequences (competitive advantage, global market penetration, increased customer satisfaction, cost reduction) constitute the model structure. In the quantitative section, the composite reliability indices were all more than 0.7 and the convergent validity for most categories was more than 0.5. The results of the hypothesis test also indicated the complete confirmation of the relationships between the model categories with a significance level of p<0.001.
Fekret et al. (2024) studied the role of artificial intelligence marketing on increasing sales and exports of Iranian sports goods using a phenomenological approach. The phenomenological approach was carried out using the Claise method (1976), which after coding the interviews using the phenomenological method led to the identification of 9 main themes and 53 sub-themes. The 9 main themes included (accurate identification of dimensions and indicators of smart marketing, SEO development, greater use of digital marketing and content marketing strategies, increasing the quality of Iranian sports goods, proper management of advertising and sales of Iranian sports goods, use of artificial intelligence tools, coverage of neuromarketing, employment of specialized human resources, exchange of information between the marketing and sales units).
Research Methodology
The research method is applicable in terms of its purpose, and quantitative in terms of its implementation method. The statistical population of the study included sales experts and marketing specialists in electronic product export companies, which were estimated using the stratified random sampling method and the Cochran formula. Considering that the number of these people is about 1600, the sample size was considered to be 306 people. The data collection tool is a researcher-made questionnaire.
Research findings
SPSS and PLS software were used to analyze the data. The results showed that the composite reliability indices were all more than 0.7 and the convergent validity for most categories was more than 0.5. The results of the hypothesis test also indicate the complete confirmation of the relationships between the model categories with a significance level of p<0.001. Also, the GoF criterion was used to examine the overall fit of the model and measure for the structural model. The GOF value is 0.815, which indicates a strong fit of the model.
Conclusion
The present study was conducted with the aim of presenting a model for the application of artificial intelligence in the export of electronics industry products. The results of this study are in line with the results of Zolghadr et al. (2026), Fekret et al. (2024), Haghighi (2024), Rahimi Klor et al. (2024), Bandegi et al. (2024), Mao & Lu (2024), Hasan & Ojala (2024), Menzies et al. (2024), and Karamipour (2023). Menzies et al. (2024) showed that AI can be used in innovation approaches in international trade, international market selection, entry modes, foreign exchange, international human resource management, international supply chains, culture management and other topics. AI has necessitated changes in workplace settings and the need for organizational and employee adjustments in response to this technology.
Based on the research results, the following suggestion was made:
Improving logistics and smart supply chains: AI systems can identify cheaper and faster transportation routes and predict customs problems or busy ports. Companies should use this capability to reduce delivery times and transportation costs. Acknowledgments The authors gratefully acknowledge all individuals, institutions, and organizations that provided scientific, technical, administrative, or advisory support throughout this research and the preparation of this manuscript. Conflicts of Interest The authors declare that they have no financial or non-financial, personal, professional, or institutional conflicts of interest relevant to this work. Data Availability Statement The data supporting the findings of this study are available from the corresponding author upon reasonable request. AI Use Statement The authors declare that no generative artificial intelligence (AI) or AI-assisted technologies were used in the preparation, writing, or editing of this manuscript. Funding The authors declare that no funds, grants, or other financial support were received during the conduct of this research or the preparation of this manuscript. Ethics Approval All procedures performed in this study were conducted in accordance with the ethical standards of the responsible institutional and national research committee.