Искусственный интеллект в стратегировании

Organizational learning is important in today’s dynamic and discontinuous environment of change.This study attempts to test the theoretical underpinning that organizational learning is related to competitive advantage. Through personal interviews and mailed surveys, 94 responses from employees of the petroleum industry were used in the regression analysis. Empirical findings show that theoretically assumed dimensions of the learning organization and organizational learning contribute significantly towards the achievement of competitive advantage. Multiple regression analysis showed the existence of significant relationships between seven independent variables and the dependent variable of competitive advantage. Strategic thinking and team learning explained 59.2% and 22.3% of variance respectively in achieving a competitive advantage. A discussion of the results along with implications and recommendations are provided.

In the workplace of the twenty-first century, there has been a rise in interest in organizational learning. The purpose of this research is to add to the body of knowledge regarding organizational learning, by analyzing how TQM affects organizational learning. Also in this study, we intend to indicate the influence of strategic thinking as moderator on the relationship between TQM and organizational learning. Data were gathered from respondents (N = 306) who were employed in industrial sectors and registered with the Amman stock exchange in Jordan. Furthermore, the study results support the assumption that TQM affects organizational learning. Moreover, the findings confirmed that strategic thinking plays a moderating role in the relationship between organizational learning and TQM, supporting the moderation hypothesis as well. After presenting theoretical and practical implications of the research, limitations and future direction are discussed.

Organizational learning theory is motivated by the observation that organizations learn by encoding inferences from experience into their behavior. It seeks to answer the questions of what kinds of experiences influence behaviors, how and under what circumstances behaviors change, and how new behaviors are stabilized and have consequences for organizations’ adaptation to their environment. Organizational learning research has as key mechanisms innovations and other triggering events that lead to major behavioral change, knowledge accumulation and experimentation that encourage incremental change, and interpretations that guide each of these processes. Organizational learning research has gained a central position in organizational theory because it has implications for organizational behaviors that also affect other theoretical perspectives such as institutional theory, organizational ecology, and resource dependence. Key research topics in organizational learning and adaptation are (a) organizational routines and their stability and change, (b) performance feedback and its consequences for organizational search and change, (c) managerial goal formation and coalition building, (d) managerial attention to goals and organizational activities, and (e) adaptive consequences of learning procedures. Each of these topics has seen significant research, but they are far from completing their empirical agenda. Recently, organizational learning research has been very active, especially on the topics of routines, performance feedback, and attention, resulting in a strong increase in learning and adaptation research in management journals.

Purpose This viewpoint article presents perspectives on the intersection of AI and the diverse elements of Psychological Capital (PsyCap) and is a foundation for discussion on this juncture. It also exhibits the advantages of integrating AI and PsyCap in learning organizations and the challenges and future directions associated with the same. Design/methodology/approach The article exhibits the juncture of Artificial Intelligence (AI) and Psychological Capital (PsyCap) in context to learning organizations. It also highlights the various AI tools that can be used to manage diverse PsyCap attributes in learning organizations. The approach is to present a starting point for discussion on the intersection of the two concepts and how learning organizations can leverage them to create sustained competitive advantage. Findings Artificial Intelligence Systems can be used to enhance self-efficacy, promote optimism, cultivate hope and build resilience in learning organizations. A strategic approach with continued evaluation of AI tools is the key. However, challenges like privacy and unethical issues, biasness and management of human- AI intervention have to be taken into consideration for effective integration of AI tools and Psychological Capital. Originality/value The research in the domain of Artificial Intelligence and related technologies is not a new phenomenon. However, there is a possibility to understand that AI is not a competitor to human ingenuity but a facilitator to it. Through this article an effort is made to ignite the conversation on the topic and it can be a starting point for future research on the integration of the two perspectives.

Mastering company planning and strategy development was essential in today’s dynamic corporate landscape to ensure long-term success and sustain a competitive advantage. This reviewed article consolidates current research and case studies to examine complete strategies that organisations use to improve their strategic capabilities. The research highlighted the significance of incorporating real-time data analytics, adaptable goal-setting frameworks, and new technologies like artificial intelligence and machine learning into strategic planning processes. Agile approaches were crucial for iteratively implementing plans, and promoting the capacity to adapt and respond to changes in the market. Moreover, the study emphasised the need to implement continuous evaluation frameworks that included real-time monitoring and feedback loops to guarantee the continual improvement of strategies. This study also investigated the impact of leadership and organisational culture on promoting strategic agility and continuous improvement. The results indicated that an integrated strategy, in which research, goal-setting, execution, and evaluation activities were interrelated, was crucial for the development of efficient and relevant corporate strategies. By adhering to these suggestions, organisations may more effectively traverse the intricacies of the contemporary industry, guaranteeing long-term success and a strong competitive edge. Keywords: Business planning, Strategic Development, Competitive advantage, Real-time data analytics, Flexible goal-setting.

Community Education institutions face numerous challenges, including fluctuating resources and evolving educational policies, which necessitate strong organizational adaptation capacity. This systematic literature review examines how organizational learning processes support organizational adaptation in community Education institutions, identifies the challenges faced, and explores strategies to enhance adaptability. Using the PRISMA framework, searches were conducted in Scopus and ERIC databases, focusing on peer-reviewed articles published between 2020 and 2024. A total of 7 studies from Scopus and 25 from ERIC were analyzed. Data were extracted, and a thematic analysis was conducted to identify recurring themes related to organizational learning and adaptation. Results show leadership support, knowledge sharing, and a strong learning culture as key adaptive mechanisms. This study fills a gap in the literature by exploring organizational learning in community Education settings and offers both theoretical contributions and practical implications for institutional leaders and policymakers. The study highlights the importance of fostering a learning culture and leadership engagement to drive institutional adaptability. Policymakers should consider providing targeted support for professional development in community Education, recognizing its critical role in sustaining organizational adaptability. The review further emphasizes the need for educational policies that are not only regulatory but also capacity-building, enabling institutions to develop internal learning mechanisms that enhance resilience and responsiveness to societal changes.

The escalating urbanization and industrial activities in cities have significantly impacted air quality, posing health risks and environmental challenges that demand innovative solutions. This review systematically explores the integration of artificial intelligence (AI) and Internet of Things (IoT) sensors within smart cities, focusing on their role in real-time air quality monitoring and dynamic response mechanisms. By adhering to PRISMA guidelines, we analyze recent advancements in AI-driven automated control systems, which utilize IoT sensors to continuously monitor pollutants, including nitrogen dioxide (NO₂), sulfur dioxide (SO₂), carbon monoxide (CO), and particulate matter (PM). The data gathered by these sensors feed into AI algorithms that facilitate immediate, adaptive responses, such as modifying traffic light sequences to alleviate congestion and notifying nearby facilities to adjust emissions during high pollution periods. This review synthesizes findings on the effectiveness, limitations, and scalability of these systems, highlighting key challenges like sensor data accuracy, privacy considerations, and the infrastructure required for city-wide deployment. The paper concludes by emphasizing the transformative potential of AI and IoT in fostering sustainable urban environments and presents recommendations for future research and policy improvements to optimize smart city air quality management.

Both human brain and computer (electronic brain) could process data and do some cognition and computation tasks. Is the cognition of human brain equal to the computation of computer? It is obviously not. In this talk, the relationships of brain cognition models and intelligence computation models are summarized into four different types, that is, human brain cognition inspired intelligence computation (BCIIC), intelligence computation without human brain cognition (ICOBC), intelligence computation assisted human brain cognition (ICABC), and the integration of human brain cognition and intelligence computation (BC&IC). There are three paradigms in traditional artificial intelligence (AI) studies, that is, symbolism AI, connectionism AI, and behaviorism AI. The physical symbol system hypothesis is used in the symbolism AI. Human brain cognition is taken as a kind of symbolic processing, and the processes of human thinking are computed by symbol in the symbolism AI [1,2]. The connectionism AI relies on the bionics to simulate human brain. In the connectionism AI, neuron is taken as the basic unite of human thinking, and the intelligence is taken as the result of interconnected neurons competition and collaboration [3,4]. In the behaviorism AI, intelligence depends on the perception and behavior, “Perception-action” model is used, and intelligence may not require knowledge, knowledge representation and knowledge reasoning [5]. The symbolism AI and connectionism AI are two different types of human brain cognition inspired intelligence computation, while the behaviorism AI is a representative case of intelligence computation without human brain cognition. Usually, AI researchers get some inspiration from human brain cognition in their studies. On the other way, intelligence computation could also assist human brain cognition studies. The bidirectional cognitive computing model (BCC) is such a case. It studies the bidirectional transformations between the intension and extension of a concept. It is used to simulate some human brain cognition tasks such as learning and recognition [6,7]. Cognitive computing is one of the core fields of artificial intelligence [8,9]. Data-driven granular cognitive computing (DGCC) is an example of the integration of human brain cognition and intelligence computation [10,11]. It takes data as a special kind of knowledge expressed in the lowest granularity level of a multiple granularity space. It integrates two contradictory mechanisms, namely, the human’s cognition mechanism of ‘‘global precedence’’ which is a cognition process of ‘‘from coarser to finer’’ and the information processing mechanism of machine learning systems which is ‘‘from finer to coarser’’, in a multiple granularity space. It is data-driven cognitive computing model. The integration of human brain cognition and intelligence computation would be an important research topic of artificial intelligence. Some scientific research issues of the integration of human brain cognition and intelligence computation are discussed based on DGCC.

Although cultural heritage management is an inherently retrospective discipline, there is a need for strategic forward thinking. Too many valuable heritage places have been lost because they are not recognized and assessed in time. As cultural heritage management begins to examine modern structures and sites, this paper takes strategic thinking in cultural heritage management one step further and addresses the management of artifactual material created by our closest relatives, the great apes. Given the increasing understanding that chimpanzees have cultures and traditions in tool use, there is a need to recognize their heritage value in reference to human evolution.Expanding the concept of nonhuman heritage into the future, it is now also time to explore how to deal with the artifacts that the first artificial intelligence (AI)-imbued, self-reflecting robots will create. By extension, which artifacts will be kept along the way? The contemplation of the role of nonhuman heritage will ultimately foster a reappraisal of human heritage. The article outlines some of the conceptual issues that must be addressed if our heritage is to have an ethical future.*Institute of Land, Water and Society. Email: e-mail dspennemann@csu.edu.au

The article examines the role of commercial activity in the goods and services market and proves that it is a complex multifunctional system that includes planning, organization, coordination and control of the processes of procurement, sales, pricing, assortment formation, logistics, promotion and after-sales service. Its effectiveness directly affects the financial results of the enterprise, the level of its competitiveness, business reputation and stability in a changing market environment. In conditions of unstable demand, increasing costs and changing consumer preferences, it is commercial activity that ensures the adaptation of enterprises to new economic realities. The article proves that the goods and services market is characterized by a high level of saturation, diversity of supply and strengthening of the role of the consumer as an active participant in market relations. Buyers are increasingly focused not only on price parameters, but also on product quality, service, availability of information, speed of service and level of communication with the seller. In this regard, enterprises are forced to implement customer-oriented approaches, apply modern marketing tools, and form flexible commercial strategies aimed at long-term consumer retention. It is demonstrated that a feature of the modern stage of development of commercial activity is the growth of the role of the service sector and the integration of product and service components into a single system of creating consumer value. Most enterprises strive not only to sell goods, but also to offer a comprehensive solution that includes additional services, consulting support, warranty and post-warranty service. It is proven that such an approach significantly complicates commercial processes, but at the same time opens up new opportunities for differentiation and increasing customer loyalty. An analysis of the scientific literature was conducted to determine the impact of the development of information technologies and e-commerce on commercial activities in the market of goods and services. The article proves that the relevance of the study of commercial activity is due to the need for rational use of material, financial and labor resources of the enterprise. Optimization of purchasing activities, reduction of commercial costs, increase in inventory turnover and effective pricing are key tasks, the solution of which directly affects the level of profitability and financial stability of business entities. In this context, scientific substantiation of methods and mechanisms of commercial activity management acquires special importance. In a competitive environment, commercial activity is an important factor in the formation and maintenance of competitive advantages of an enterprise. This is especially true for retail and service enterprises, where competition is clearly local in nature.

This qualitative study explores how professionals across industries are using generative artificial intelligence (AI) and what their experiences suggest for higher education. Through interviews with 10 professionals, the study investigates: (1) What forms of AI-related training are available to employees? (2) How do professionals describe existing policies (or their absence) governing AI use in the workplace? (3) In what ways are AI tools being adopted across sectors, and for what types of tasks? and (4) How do professionals perceive AI’s influence on efficiency, expectations, and required skills? Four key findings emerged: participants engaged in self-led AI learning through experimentation; organizational policies were absent or just emerging; adoption patterns varied by role, sector, and initiative; and AI tools were primarily used for content generation, data-informed decisions, and overcoming creative blocks, reshaping job expectations toward adaptability, creativity, and strategic thinking. Findings suggest that generative AI is transforming tasks and altering how professionals learn, decide, and collaborate. In response, four implications for higher education are: rethinking AI learning through experiential and playful approaches, building AI awareness and ethical confidence, supporting thoughtful adoption across disciplines, and fostering skills for effective human-AI collaboration. Preparing students will require technical exposure and critically engaged, ethically grounded practice.

The purpose of the article is to analyze and assess the situation of the market for environmental goods and services in the EU as well as the formation of its support and regulation system. Methodology: The market for environmental goods and services has experienced the dynamic growth and development in the last two decades, especially in economically developed countries. The research concerns the peculiarities of the development of the organic products market, sources of support for the development of organic production in the EU. The research uses the desk research method, which provides a possibility of using a wide range of materials in various areas and periods. Additionally, relevant legal acts, official documents and reports were used in the study. Results of the research: The structure of certified agricultural lands in the EU is thoroughly analyzed in the article. Emphasis is placed on the study of the EU market for organic products development peculiarities, which include a constant growth of demand for these products and their rising prices. The main elements of the ecological goods and services market regulation system are also characterized. The conclusions contained in the article refer to the possibility of using one of these instruments: financing from the European Agricultural Guarantee Fund and the European Agricultural Fund for Rural Development, crediting, venture financing, green bonds, fundraising, crowd funding, crowd landing as well as own means of enterprises. Generally, financial support should be more flexible and tailored to the needs of beneficiaries.

This paper introduces the 4C (compile, curate, challenge, contemplate) pedagogical framework for integrating generative artificial intelligence (AI) into advertising education through adaptive case studies. Our framework positions AI not as a shortcut to answers, but as a tool for supporting creativity, strategic thinking, and problem-solving. We describe how educators can use AI to generate flexible, goal-oriented case studies, and how students can draw on AI tools when developing solutions. To illustrate the application of the 4C framework, we provide a concrete step-by-step demonstration (with AI prompts) of how the approach can be implemented in practice. In doing so, the approach aims to cultivate an adaptive mindset—shifting attention from what AI can replace to what new possibilities it enables. By aligning classroom experiences with real-world demands, this model helps students move toward AI fluency, preparing them to create meaningful value in a rapidly evolving advertising industry.

The concept of Artificial intelligence has gained significant popularity over the past few years, particularly with the introduction of Open AIs ChatGPT Model, which has made the technology of artificial intelligence more accessible to people worldwide.Since the 1950s, chess has been used as a testing ground for artificial intelligence. Deep Blue set history in 1997 when it defeated GM Garry Kasparov over the course of a six-game match as it marked the turning point of artificial intelligence being able to master complex strategic thinking. By analyzing previous researches regarding Chess Artificial Intelligence, This paper suggests that Artificial Intelligence had multiple applications in training Elite Chess Players, providing great advantages which can improve their Decision-making. Thinking and Time-Management Skills, ultimately benefiting their game play in tournaments. This paper provides an overview of the evolution of Chess Artificial Intelligence, identifies the key technologies of Chess Artificial intelligence, and introduces the strategies to apply artificial intelligence in training elite chess players, aiming to provide valuable insights for relevant researchers and practitioners.

Purpose. The study aims to understand artificial intelligence as a socio-cultural phenomenon and its impact on education, where the spiritual sphere of humanity, moral norms, values, and human cognitive abilities are preserved, transferred as well as reproduced. A new discourse on the interaction of artificial and authentic human intelligence becomes inevitable, which has led to a situation of uncertainty. Changes in the socio-cultural environment under the influence of artificial intelligence increase potential threats to the educational space, which stimulates to find the ways to eliminate them. Theoretical basis. Various approaches of classical and postmodern philosophical heritage were taken as a theoretical basis for the research. The originality of the study is in the interpretation of artificial intelligence as a modern form of alienation of essential human characteristics in the socio-cultural context of information technology. The expansion of artificial intelligence raises awareness of the existential threat to the basic socio-cultural, moral and ethical principles of humanism. It is proved that various forms of alienation in the current existing socio-cultural space are typical of our reality, which changes the system of values, moral principles, and social organization of the community. Conclusions. In conclusion, it is proved that AI is a natural stage of scientific and technological progress, which reflects its secondary, derivative nature from human (authentic) intelligence. Human intelligence will always have advantages over AI due to its ability to create, communicate socially and culturally, and be emotional. The dilemma of the counterbalance between human and artificial intelligence is perceived mainly at the emotional level of people. The millennial understanding of the primacy of the creator over his creation can traditionally overcome this contradiction. The universality of human thinking is an undeniable advantage of human intelligence and a guarantee of its, i.e. our, priority.