New Approaches in Management and Marketing

Abstract The aim of this study is to determine the relationships and hierarchical levels of optimal risk control factors in the medical laboratory supply chain using an Interpretive Structural Modeling (ISM) approach. In terms of implementation, this research adopts a mixed-methods design (qualitative–quantitative); and in terms of purpose, it is an exploratory study conducted through a descriptive–survey method. The statistical population of the study consisted of 15 senior managers and experts working in medical laboratories across the supply chain, selected by the snowball sampling method. Data were collected through semi-structured interviews. For data analysis, the Fuzzy Delphi method, Interpretive Structural Modeling (ISM), and MICMAC software were employed.
The results of the interpretive structural analysis using the exploratory model indicated that the proposed model consists of four main factors and thirteen sub-factors identified across four levels. The fourth level involves supply chain risk identification, including the dimensions of financial and legal risks, quality risks, and supplier and logistics risks. The third level includes failure mode and effects analysis, encompassing the identification of weaknesses, analysis of failure effects, and risk prioritization. The second level involves risk response and management strategies, including supplier diversification, emergency planning, and quality control supported by information technology. Finally, the factors and dimensions identified in the previous three levels lead, at the first level, to achieving optimal risk control, which includes risk and failure reduction, increased supply chain efficiency, continuous improvement, and performance monitoring.
Introduction
The extensive use of medical and laboratory technologies requires a significant number of resources for the procurement of equipment and materials; an issue that must be given considerable attention in these organizations. It is evident that if the procurement of equipment and materials is carried out without a precise and structured mechanism, the quality of purchases will not align with the actual needs of healthcare centers. Therefore, the procurement process in medical centers is of great importance and must be continuously monitored. Risk assessment is a systematic process for measuring quantitative and qualitative hazards associated with materials, processes, activities, personnel, equipment, and the environment (Rahmanifar, 2014).
Utilizing the findings of this research on the supplier side can lead to the development of structures that ensure timely procurement and provide an understanding of risks and potential failure points in the laboratory supply process. As a result, suppliers can devise various methods to meet laboratory needs. Considering that the researcher is also active in the laboratory supply sector, if the findings are validated, they can be presented to the laboratory community through multiple sessions, thus contributing to the dissemination and expansion of the research results. Given that multiple methods and individuals are involved in the procurement of laboratory necessities, this analytical approach can also be applied in this study, representing one of its innovative aspects.
Failure Mode and Effects Analysis (FMEA) is one of the most widely used tools for enhancing the reliability and safety of products or processes (Radpour & Karbasian, 2011). This tool is particularly used in the design and development of new products (Fattahi, 2009). Various techniques exist for risk identification, and no single method serves as the best approach; instead, an appropriate combination of techniques should be utilized. Moreover, evaluating risks solely based on their probability of occurrence and severity of impact is insufficient, as these two criteria do not fully capture all influential aspects of risk. FMEA is an analytical technique that combines technology and expertise to prevent the procurement of substandard products and protect the reputation of the organization (Radpour & Karbasian, 2011).
Therefore, aligned with this objective, the main research question of the present study is: What are the relationships and hierarchical levels of optimal risk control factors in the medical laboratory supply chain using an Interpretive Structural Modeling approach?
Theoretical Framework
Risk Management
Supply chain risk management focuses on controlling risks that affect the efficient flow of information, materials, and products across different segments of the supply chain. Risk management is a key component of supply chain management; and given the diverse objectives within supply chains, it can be utilized when risk is considered a multidimensional phenomenon. Factors such as uncertainty in supply and demand, market globalization, shorter technology and product life cycles, and the increasing use of outsourcing have been identified as major reasons for the importance of supply chain risk management (De Oliveira et al., 2024).
Wang et al. (2025), in their study on Strategic Behavior in Multi-Criteria Sorting with a Trust-Relationship-Based Consensus Mechanism: Application to Supply Chain Risk Management, examined strategic behavior using a cost-based consensus mechanism within a social trust network. First, a piecewise cost function dependent on trust relationships was formally defined and formulated, taking into account the influence of trust on preference adjustments required to achieve consensus sorting. Based on this trust-dependent cost structure, a strategic preference manipulation model was proposed to enable strategic behavior in consensus sorting by minimizing the adjustment costs incurred by decision-makers. Considering that the social trust network affects strategic manipulation, an extended collaborative strategic manipulation model was introduced to guide decision-makers in adjusting both their preferences and their social trust networks. Furthermore, the practical applicability of the proposed strategic manipulation models was demonstrated through a supply chain risk management case study, and their performance was validated through simulation analysis.
Mobo et al. (2025) investigated Supply Chain Risk Management in the Trade Sector: A Modern Perspective. Their findings indicate that studying supply chain sustainability strategies is crucial, especially when the risks faced by commercial farms within the supply chain are increasingly complex, diverse, and sudden—making prediction and probability assessment more difficult than ever. This underscores the significance of supply chain management. It was found that in order to gain competitive advantages within the market, the trade sector must improve supply chain management practices, which leads to reduced costs from production to delivery, optimized timing, and enhanced customer satisfaction. Their study also concludes that an effective risk assessment mechanism in the supply chain for the trade sector must serve as an appropriate tool for evaluating the effectiveness of risk management from the perspective of all business segments involved in the supply chain.
Research Methodology
This study was conducted using a mixed-methods approach (qualitative–quantitative) and, in terms of purpose, followed an exploratory and descriptive–survey design. The statistical population consisted of 15 senior managers and experts working in medical laboratories across different stages of the supply chain, selected through the snowball sampling method. Data were collected by semi-structured interviews.
Research Findings
For data analysis, the Fuzzy Delphi method, Interpretive Structural Modeling (ISM), and MICMAC software were used. The results of the interpretive structural analysis using the exploratory model revealed that the model includes four main factors and thirteen sub-factors distributed across four hierarchical levels.
The fourth level involves supply chain risk identification, including financial and legal risks, quality risks, and supplier and logistics risks. The third level consists of failure mode and effects analysis, including identifying weaknesses, analyzing failure impacts, and prioritizing risks. The second level encompasses risk response and management strategies, including supplier diversification, emergency planning, and quality control supported by information technology.
Finally, the factors and dimensions identified in the previous three levels lead, at the first level, to achieving optimal risk control, characterized by risk and failure reduction, increased supply chain efficiency, continuous improvement, and performance monitoring.
Conclusion
The present study aimed to determine the relationships and hierarchical levels of optimal risk control factors in the medical laboratory supply chain using an Interpretive Structural Modeling approach. The results of this research are consistent with the findings of Wang et al. (2025), Mobo et al. (2025), Shishehgarkhaneh et al. (2024), Choudhary et al. (2023), Ganesh & Kalpana (2022), Qaraghanabadi & Sarkardeh (2024), Arab Cham Khalifa & Alavi (2023), and Fokerdi & Talavari (2021). Mobo et al. (2025) demonstrated that an effective risk assessment mechanism in the supply chain for the commercial sector must serve as an appropriate tool for evaluating the effectiveness of risk management from the perspective of all business units involved in the supply chain. Based on the findings of the study, the following recommendations are proposed:
Organizations should prioritize reducing risks and preventing failures in order to achieve optimal risk control. Implementing preventive processes, continuously analyzing risks, and executing proactive programs can help reduce the likelihood of failure causes and, in turn, ensure the efficiency and continuity of the supply chain.

Abstract The present study aimed to level the strategic compliance factors based on the development of interactive behaviors in the tax affairs organization. The present research method is qualitative and utilizes the interpretive-structural modeling (ISM) method. The research population consisted of 21 managers and experts of the country's Tax Affairs Organization selected based on purposive sampling via the data saturation technique. In this study, data were collected by a strategic compliance questionnaire based on the development of interactive behaviors (researcher-made) and analyzed by structural-interpretive modeling and MICMAC analysis. The results showed that 16 factors (transformational leadership, behavioral competencies, tax education, transparency, taxpayer satisfaction, incentives, mutual understanding, crimes, electronic services, system legitimacy, monitoring, two-way interaction, trust, justice, belief in the government, citizen participation) are effective on strategic compliance based on the development of interactive behaviors and; based on the interpretive-structural model, it includes three levels of independent or key, relational and dependent variables. The monitoring and incentives variable, as an independent and key variable, had the least influence and the most dependence compared to the variables considered at other levels in the model.
Introduction
Today, tax organizations around the world face numerous challenges such as tax evasion, lack of transparency in tax processes, and taxpayer resistance to laws (Ramadanty et al., 2025). These challenges are especially exacerbated in developing countries, such as Iran, due to weak tax infrastructure and lack of efficient interactive models. In Iran, the Tax Administration, as one of the main pillars of government financing, faces problems such as high tax evasion rates, taxpayer dissatisfaction, and inefficiency in interactive processes. These issues have not only reduced tax revenues, but also undermined public trust in the tax system (Hosseini et al., 2022). In recent years, numerous studies have examined strategies for improving tax compliance. Some of these studies have focused on coercive approaches that use tools punitive and supervisory approaches, while others have focused on trust-based approaches that rely on building trust and voluntary participation of taxpayers. However, research shows that combining these two approaches in a comprehensive strategic compliance model can lead to better results (Ramadanty et al., 2025). In Iran, domestic studies have also mainly focused on identifying existing problems and have less focused on designing operational models to improve strategic compliance (Karami et al., 2021). Recent research shows that interactive behaviors, including transparency, accountability and participation, can lead to increased taxpayer satisfaction and improved tax compliance rates. Many studies have emphasized that interactive and participatory approaches in tax organizations not only reduce administrative costs, but also help create an environment based on trust and cooperation. For example, in countries such as Denmark and Sweden, the use of digital technologies and transparent tax systems has increased taxpayer satisfaction and has resulted reduced tax evasion (Mukti et al., 2025). In the Tax Affairs Organization, due to its task-oriented nature, regulatory sensitivities, and formal structure, most of the followers' behaviors have been directed towards duty-based obedience rather than strategic compliance. This has led to a decrease in the organization's agility in the face of developments such as the smart tax system and comprehensive tax plans. To overcome this situation, the development of interactive behaviors between employees and managers is a necessity; because strategic compliance does not form in a vacuum and requires a context of constructive interactions, knowledge sharing, and mutual feedback. Therefore, the main question of this research is formulated as follows: Main question: How can a strategic compliance model based on the development of interactive behaviors be designed for the Tax Affairs Organization of Iran?
Theoretical foundations
Strategic alignment
Strategic alignment means aligning an organization's activities and decisions with its overall goals and strategies. This concept is proposed in tax organizations as an approach to increase the effectiveness and efficiency in implementing tax laws and regulations (Ramadanty et al., 2025). Strategic compliance not only includes compliance with laws, but also includes creating an organizational culture in which employees and managers actively strive to achieve the strategic goals of the organization. This concept is closely related to concepts such as strategic management, organizational culture, and interactional behaviors (Hosseini & Gholami, 2022).
Ramadanty et al., (2025) conducted a study titled “Optimizing Tax Planning Strategy in Mergers and Acquisitions: A Systematic Review of the Literature”. The results showed that choosing the right tax strategy is a vital tool for managing risk and optimizing the cost structure in large transactions. The findings emphasized that diversity in selected strategies allows companies to deal with legal complexities and maximize the financial value created in the merger process.
Mukti et al., (2025) conducted a study titled “Corporate Tax Compliance as a Strategy for Improving Tax Compliance of Legal Taxpayers.” The results showed that the COVID-19 pandemic had a significant impact on tax avoidance practices.
Research Method
This study is applicable in terms of its purpose, and qualitative in terms of nature and method, and aims to level the factors of strategic compliance in the Tax Affairs Organization. The statistical population in this study included 21 managers and experts of the Tax Affairs Organization selected through purposive sampling and were questioned until the step of data saturation. The data collection tool was a researcher-made questionnaire extracted the interrelationships of variables based on the opinions of experts. Data analysis in this study was carried out through the Structural-Interpretive Modeling (ISM) method to identify hierarchical relationships, and MICMAC analysis to determine the influence power and degree of dependence of variables.
Research Findings
The findings show that transformational leadership is the most fundamental cornerstone of strategic compliance in the organization, which can be achieved through improving system competencies and tools affect mid-level variables. This hierarchical chain ultimately leads to the realization of the ultimate and macro-outputs of the organization, namely trust, justice, and citizen participation at the highest level of the model. In fact, this model emphasizes that in order to achieve strategic goals, transformation must begin from the layers of leadership and behavioral competencies.
Discussion and Conclusion
The analysis of the findings indicates that in the Tax Affairs Organization, any structural transformation requires managers who, with a transformational approach, create the necessary motivation and vision in the body of the organization. This finding is in line with the rahmi et al. (2023) research that considers strategic management and leadership to be the main prerequisites for administrative reforms in tax systems.
The behavioral competencies variable is also placed at the fundamental level of the model. It is concluded that technical knowledge alone is not enough to achieve strategic compliance and that the organization's employees must be equipped with soft skills and interactive behaviors. This finding is consistent with the results of Taling et al. (2021) regarding the importance of “perceived behavioral control.”
The tax education variable acts as a link variable at the next level of the model. The analyses show that education is the intermediary that transforms fundamental capabilities (leadership and competence) into perceptual outputs. From the research perspective, tax education includes both empowering employees to act professionally and making taxpayers aware of the rules. This finding is consistent with the view of Mukti et al. (2025) that emphasizes educational strategies to manage social norms and reduce taxpayer resistance.
The “transparency” variable in the middle layer of the model indicates that strategic compliance is impossible without clarity in processes. Analysis of the findings indicates that the more transparent the tax administration is in explaining the rules and how taxes are spent, the less ambiguity taxpayers will have and the more willing they will be to cooperate voluntarily. This result is consistent with the findings of Rahmi et al. (2023) who consider administrative transparency to be a critical outcome of digital reforms and modernization of tax systems.
According to the research model, mutual understanding is defined as a situation in which the organization and the taxpayer reach a common understanding of each other's rights and obligations. It is concluded that mutual understanding bridges the gap between government expectations and taxpayers' economic realities. This finding is consistent with Mukti et al. (2025) research on the importance of managing taxpayers' subjective and social norms to improve tax compliance.
In this layer, the variable of taxpayer satisfaction appears as a behavioral outcome resulting from transparency and mutual understanding. The analyses show that taxpayer satisfaction is not simply a positive feeling, but rather an assessment of the fairness of the services received from the organization. This finding is consistent with the theory of planned behavior in the taling et al. (2021) research; because they introduce a positive "attitude" as one of the three pillars of the formation of strategic compliance intention.
In the extracted model, penalties and incentives are included as control variables along with satisfaction and transparency. It is concluded that strategic compliance is not achieved only with rewards, but also requires a fair system of punishments and incentives to adjust taxpayers' behaviors towards the organization's goals. This finding is consistent with tax optimization strategies in Ramadanty et al. (2025) research that emphasize the balance between policy instruments.
Electronic services at higher levels of the model play an accelerating role in strategic compliance. The findings show that the electronicization of services reduces physical interaction and strengthens procedural justice by eliminating intermediaries. This result is fully consistent with the view of Rahmi et al. (2023) that considers the implementation of modern tax systems as a strategy for efficiency and reducing corruption.
The analysis of the system legitimacy variable shows that when electronic services and transparency are combined, the organization becomes legitimate in the eyes of society. Legitimacy makes taxpayers accept tax laws not as an imposition, but as a social duty. This finding is consistent with Taling et al. (2021) research in which organizational legitimacy greatly reduces monitoring costs.
In the present model, monitoring is upgraded from a police action to a systemic process. It is concluded that under the shadow of electronic services, intelligent monitoring leads to more accurate identification of taxpayers and strengthens the sense of justice in society. This is consistent with the findings of Ramadanty et al. (2025) in the field of optimizing monitoring strategies to achieve maximum tax compliance.
The “two-way interaction” variable indicates the transition from a prescriptive tax system to a participatory tax system. In this layer, the organization and the taxpayer are in a continuous feedback loop. This finding is consistent with Mukti et al. (2025) research that emphasizes the importance of social pressures and interactional connections in improving tax behavior.
These three variables, as high-level outputs of the model, complete the causal chain of strategic compliance. The final conclusion shows that justice and trust are not accidental, but are direct consequences of legitimacy and interactions at the lower levels. As Taling et al. (2021) has pointed out, trust in the system minimizes the risk of non-compliance and strengthens faith in the government.
Citizen participation is standing on the highest level of the ISM model. This variable is known as the end of strategic compliance; a situation in which paying taxes has become part of the taxpayer's citizenship identity.

Abstract Abstract This study aims to design a comprehensive and dynamic model to explain organizational resilience in Iranian small and medium-sized enterprises. Given that these companies operate in a unique ecosystem of permanent crisis, traditional resilience models based on cross-sectional shocks seem inadequate to explain their dynamics. This research is exploratory and theoretical in nature and is based on the interpretive paradigm. A mixed methodological approach including metasynthesis as the main method and bibliometric analysis as a complementary method was used, and bibliometric data were extracted from 953 articles in Scopus and analyzed with VOSviewer software. After a systematic search of reliable databases and screening, 67 key articles were selected and synthesized and integrated through a content analysis process. Qualitative data analysis led to the identification of 460 unique conceptual codes that were categorized into 15 organizing themes and 5 overarching themes including: “1. Drivers and enabling factors, 2. Multilevel capabilities (individual/entrepreneurial, organizational, network/institutional), 3. Dynamic process cycle (anticipation, exposure, learning), 4. Resilient strategies (defensive, adaptive, offensive), and 5. Heterogeneous outcomes”. The main finding of the research is “A dynamic and multilevel model of organizational resilience in a permanent crisis environment,” which conceptualizes resilience as an evolutionary meta-capability. This research presents the first comprehensive and dynamic theoretical framework to explain resilience in the Iranian perma-crisis ecosystem, and its main innovation lies in conceptualizing resilience as an evolutionary process for the strategic transition from survival to evolution and showing the mechanisms of this transition. Introduction The business landscape is witnessing a paradigm shift as it enters an era of constant turmoil and unpredictable disruptions. This environment, described as the “Woka world”, has seriously challenged the fundamental principles of traditional strategic management. In these circumstances, the survival and sustainable growth of organizations requires a capacity beyond short-term resistance, known in the literature as the concept of organizational resilience (Chi et al., 2025). The understanding of the concept of resilience has evolved from the traditional “go back” perspective to the new “leap forward” paradigm, emphasizing strategic learning and transformation (Hernes et al., 2025). However, the existing literature suffers from three gaps: theoretical fragmentation and lack of integrated models, a homogeneous view of resilience pathways, and a lack of sensitivity to context. Much of the research has examined resilience in response to episodic crises and has not been able to explain it in the context of a permanent crisis (permacrisis), a structural situation in which multiple and intertwined crises have become the norm (Maalouf et al., 2025). This concept accurately describes the unique nature of the Iranian business environment. Iranian companies, especially small and medium-sized enterprises, operate in a multi-crisis ecosystem shaped by a combination of factors such as economic and institutional instability, international sanctions, social unrest, geopolitical tensions, and infrastructure challenges (Shabani et al., 2025). Numerous pieces of evidence, including the National Business Environment Monitoring Reports and the unprecedented rise in the Uncertainty Index, confirm this turbulent ecosystem. These conditions render resilience models developed in more stable environments ineffective. At the heart of this ecosystem, SMEs, as the driving force of the economy, face a paradox: on one hand, they are more vulnerable due to resource constraints; and on the other hand, they have a higher potential for agility due to their flexibility (Koporcic et al., 2025). How this potential is activated in Iran’s high-risk context is a question that, despite valuable domestic research, still requires a comprehensive model based on systematic knowledge synthesis. Accordingly, this study aims to design and present a comprehensive, dynamic, and multi-level model of organizational resilience for SMEs in Iran’s perma-crisis ecosystem. This article conceptualizes resilience as an evolutionary meta-capability for the transition from survival to evolution, moving beyond a static perspective. To achieve this goal, this research uses meta-synthesis and bibliometric analysis to systematically synthesize previous researches to arrive at a new and context-sensitive theoretical framework and answer the fundamental question: "What are the dimensions and components of a comprehensive model of organizational resilience in Iranian small and medium-sized enterprises in a permanent crisis ecosystem?" The theoretical framework Organizational Resilience Hepfer & Lawrence (2022) defines resilience as “the ability of an organization to anticipate, respond to, recover from, and learn from adversity”. Organizational resilience is a multilevel phenomenon. The existing literature generally distinguishes three key levels of resilience: individual, organizational, and network/institutional (Hillmann & Guenther, 2021). 1. Individual level: This level focuses on the psychological resilience of employees, managers, and especially entrepreneurs (Hartmann et al., 2022). In the context of small and medium-sized enterprises, the individual resilience of the entrepreneur plays a vital and pivotal role in guiding the entire organization’s response to a crisis (Leonelli et al., 2024). 2. Organizational level: This level, which is the main focus of this research, addresses the resources, processes, capabilities, and structures that allow the entire organization to cope with disruptions (Barasa et al., 2018). 3. Network/Institutional Level: This level examines resilience beyond the boundaries of an organization and focuses on the quality of the institutional environment, supportive policies, and the organization’s relationships with external actors and stakeholders (Koporcic et al., 2025). Koporcic et al. (2025) in a comprehensive umbrella review, identified key barriers to resilience in SMEs as constraints related to firm size and greater vulnerability to shocks, financial constraints and ineffective leadership and lack of crisis management skills; and key coping strategies included business continuity planning, organizational culture, technology adoption, proactive manufacturing, and strategic cooperations. De Waal et al. (2025) identified four heterogeneous resilience paths (incomplete recovery and survival, performance recovery, leapfrogging, explosive leap) in a longitudinal study of Ukrainian companies in war conditions, which shows that survival in critical conditions is itself a form of resilience. Research Methodology This research is exploratory and theoretical in nature, based on an interpretive paradigm, and uses the meta-synthesis and bibliometric analysis methods. Research Findings Data analysis was conducted using the meta-synthesis method as the main method and bibliometric analysis as a complementary method. Also, bibliometric data were extracted from 953 articles in Scopus and analyzed with VOSviewer software. After a systematic search of reliable databases and screening, 67 key qualitative and conceptual studies were finally selected and synthesized and integrated through a content analysis process. The results showed that 460 conceptual codes were classified into 15 organizing themes and 5 overarching themes including “drivers and enabling factors, multilevel capabilities, dynamic process cycle, resilient strategies, and heterogeneous outcomes.” The main finding of the research is “A dynamic and multilevel model of organizational resilience in a permanent crisis environment”. This model conceptualizes resilience as an evolutionary meta-capability that emerges from the dynamic interaction between the reservoir of multilevel capabilities and the flow of a continuous learning cycle and includes the following elements: 1) drivers and enabling factors (permanent crisis context); 2) the core of resilience including multilevel capabilities (individual/entrepreneurial, organizational, and network/institutional) and a dynamic process cycle (anticipation, exposure, learning); and 3) heterogeneous outcomes (from survival to explosive mutation). This model operates through strategic choices (defensive, adaptive, offensive) and evolves over time with feedback loops and an “evolutionary learning spiral”. Conclusion The present study aimed to provide a comprehensive and dynamic model of organizational resilience in Iranian SMEs in a permanent crisis ecosystem using a meta-synthesis method. The findings of this study are fully consistent with studies that emphasize the pivotal role of the leader in SMEs (Leonelli et al., 2024; Branicki et al., 2018). However, this model goes further and shows that leader resilience (including strategic intelligence and managerial competencies) is not only a driver, but also the main catalyst for activating capabilities at other levels. At the organizational level, this model is consistent with the main literature by highlighting capabilities such as agility, learning culture and digital transformation (Georgescu et al., 2024; Dowlatabadi, 2025) and shows that organizational capabilities are a platform for institutionalizing individual-level experiences and intuitions and transforming individual learning into organizational memory. At the network level, this model is also consistent with the findings of researchers by emphasizing strategic partnerships and resource dependency management (Koporcic et al., 2025). In the Iranian context, where formal institutions are weak and unreliable, informal and trust-based networks (social capital) become an alternative infrastructure for business. At the macro level, the resilience of small and medium-sized enterprises affects the resilience indicators of society, and supportive government policies can lead to strengthening the country's business ecosystem. This research, by moving beyond simplistic models, attempted to open the black box of resilience in the unique context of Iran's permanent crisis.

Abstract This study was conducted with the aim of investigating the impact of business culture on the role of artificial intelligence in developing business strategies. The research method is applicable in terms of its purpose, quantitative in terms of its implementation method, and descriptive-correlational in terms of its nature and method. The statistical population of the study included all managers and employees of Shasta Company, which was 315 employees and managers by simple random sampling method. A standard questionnaire based on a 5-point Likert scale was used to collect research data. The content validity of the tool was confirmed by specialists and experts, and Cronbach's alpha and composite reliability were used to measure the reliability of the tool. By distributing the questionnaire, the validity of the tool was measured with three methods: construct validity (external model), convergent validity (AVE), and divergent validity. The AVE value for all variables should be greater than 0.5. SPSS and PLS software were used to analyze the data. Research findings show that business culture has an impact on AI. AI has an impact on business organizational model. AI has an impact on strategic marketing decision management. AI has an impact on strategic decision management.
Introduction
In the digital age, the business world requires shorter response times and greater attention to competitive landscapes that can change faster than ever. In this context, many companies are embracing new technologies with the aim of achieving high performance and competitive advantage (Mao, 2025). Among these technologies, AI has occupied a prominent position and has attracted the attention of the literature and business organizations. According to Davenport (2018), AI may be the technological force with the greatest potential for disruption in today’s evidence. Similarly, for Brynjolfsson & Mcafee (2017), AI is the most important general-purpose technology of our time, especially with regard to machine learning techniques.
However, in the past decade, the huge amount of data in various formats, which is being generated faster than ever before, has required the development of new technologies, led to an acceleration of technological progress, which includes the increase in computational processing capacity and the development of new AI techniques (Amin et al., 2025). With these advances, companies such as Netflix, Google, Airbnb, Amazon and Uber can process large amounts of data with AI and use the results to expand their reach with new products, markets and services (Ajalli et al., 2023). Given the competitive business scenario with high data volume, scarce resources and the need for speedy decision-making, many organizations are motivated to adopt AI technologies (Dhaigude et al., 2025).
Various leaders, aware that this process requires a review of business strategy, are also reformulating their strategic plans to include AI technologies (Yu et al., 2025). However, the literature shows that more research is necessary to understand the impacts of AI in planning and executing business strategies (Hajipour et al., 2023), as there is still little theoretical and empirical evidence on how to do it. However, there is a consensus on the need to create business value using AI technologies (Singh et al., 2025).
Artificial intelligence plays a disruptive and destructive role in the balance of marketing and businesses world. Artificial intelligence can rebuild the foundation of businesses and, in this way, create new and rapid revenue streams and bring sustainable competitive advantage. If, according to the classical theorists of this field, we consider strategic management as the brains of the organization and the wisdom of business leaders in saving and guiding the organization throughout its life cycle, and on the other hand, we take a look at the intensity of the entry of artificial intelligence into various sciences, the need to pay attention to artificial intelligence and its advances in strategic management will become more and more obvious to us. Although the foundation of management is almost fixed; but looking at business through the lens of artificial intelligence requires fundamental revisions in the leadership method and the use of organizational management tools. Accordingly, the question arises: what effect does business culture have on the role of artificial intelligence in formulating business strategies?
Theoretical Literature
The Role of Artificial Intelligence, Strategy Development, and Business Performance
With its ability to process and analyze large amounts of data and simulate scenarios, Artificial Intelligence provides organizations with tools that make the strategy development process more accurate, faster, and based on real data. This capability of AI enables organizations to better evaluate decision-making options and design their strategies with more confidence. In turn, optimal and data-driven strategies enhance organizational performance because they improve resource allocation, decision-making, and action execution, and as a result, the organization will be able to achieve its goals more effectively (Bagheri et al., 2023).
On the other hand, AI also directly impacts organizational performance; this technology improves the organization's ability to achieve goals and increase efficiency by reducing human errors, increasing productivity, and creating more value for customers and stakeholders (Dowlatabadi et al., 2025; Guler et al., 2024). Another important point is the role of organizational culture and business model, which act as reinforcing factors. Organizations that foster a culture of learning, innovation, and knowledge sharing can implement AI more effectively and, through it, increase the impact of AI on organizational strategy formulation and performance (Jorzik et al., 2024).
Mao (2025) reviewed the “Supply Chain Optimization Strategy and Application Method of Business Management Based on Artificial Intelligence Technology”. First, the advantages of AI-based solutions in supply chain management, especially in demand forecasting, inventory management and warehouse process automation, were investigated; and a supply chain optimization model based on genetic algorithm was built. Experimental results show that AI technology and genetic algorithm can significantly reduce supply chain costs, improve logistics efficiency and increase service levels, and confirm the effectiveness of the model in practical applications. Labin (2024) reviewed “Artificial Intelligence in Marketing: A Review of Current and Future Trends”, using a systematic literature review method. The findings of the bibliographic analysis revealed six emerging clusters of AI in marketing research, namely psychosocial dynamics, AI-enhanced dynamic market strategies, AI for consumer services, AI for decision-making, AI for value conversion, and AI for ethical marketing.
Research Methodology
This research is applicable in terms of purpose, and descriptive-correlational in terms of method. The statistical population of the research includes 420 managers and employees of Shasta Company, of whom 315 were selected as stratified random samples using the Cochran formula. A researcher-made questionnaire on a five-point Likert scale was used to collect data. The findings from the Cronbach's alpha test and composite reliability to measure the reliability of the research instrument are reported in Table 1. To examine the validity of the instrument, content validity (expert opinion survey) was used and its validity was confirmed. Then, by distributing the questionnaire, the validity of the instrument was measured with three methods: construct validity (external model), convergent validity (AVE), and divergent validity. The AVE value for all research variables should be greater than 0.5. In order to test the research hypotheses, structural equation modeling was used in the context of smart pls2 statistical software.
Research findings
The research findings show that business culture plays a key role in the acceptance and effectiveness of artificial intelligence, and organizations that promote a culture of learning, innovation, and knowledge sharing can use artificial intelligence capabilities more effectively. Also, artificial intelligence has a direct impact on the organizational model and strategic decision-making processes and provides the ability to convert big data into practical knowledge and effective decisions. In the field of marketing, artificial intelligence improves strategic decision-making and the design of targeted campaigns. Furthermore, the combination of organizational culture, business model, and AI creates an interactive cycle that enhances organizational performance and provides sustainable competitive advantage.
Discussion and Conclusion
One of the most important results of this study is that business culture has a direct impact on the effective use of AI. Organizations that promote a culture of continuous learning, innovation, cross-functional collaboration, and knowledge sharing can use AI capabilities more optimally. These findings are in line with studies by Davenport (2018) and Farah et al. (2023), who emphasize that cultural harmony and interaction between organizational units strengthen AI capabilities and shape its real value in the organization. This shows that the success of AI deployment is not limited to technological tools alone, but also depends greatly on the organizational environment and prevailing culture.
Another finding shows that AI has a direct and significant impact on the organizational model and strategic decision-making processes. Organizations that integrate AI into their structure and processes are able to transform complex and big data into actionable knowledge and effective decisions. This increases the accuracy and speed of strategic decision-making and allows the organization to allocate its resources more effectively. These results are consistent with research by Karamipour (2023) and Dowlatabadi et al. (2025), which show that implementing AI can become a core competency of the organization and significantly improve organizational performance.
Artificial intelligence also plays an important role in marketing decision-making and strategic market management. Using AI tools, organizations are able to analyze customer behavior, market trends, and competitive patterns and design their marketing strategies based on real and dynamic data. This capability allows them to respond quickly to environmental changes and perform better in dynamic markets. The findings of this study are consistent with the studies of Labin (2024) and Peltier et al. (2024), which show that AI optimizes marketing processes and helps create shared value in customer-organization interactions. The role of organizational culture and business model is as an enabling factor. Organizations that have an innovative culture and value knowledge sharing, learning, and collaboration can enhance the impact of AI on strategy formulation and performance. In other words, the success of AI-based strategies depends not only on the technology itself, but also requires organizational cultural and structural alignment. These findings are consistent with studies by Schein (2017) and Teece (2018) and show that alignment between technology, culture, and business model is essential for creating sustainable competitive advantage. 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.