Management approaches in the field of smart
Shiva sadat Ghasemi; Abbas khamseh; Seyed Javad Iranban
Abstract
In the contemporary landscape of technology-driven industries, the integration of artificial intelligence into technology scouting is imperative for enhancing innovation and sustaining competitiveness. This research aims to forge a framework for technology scouting based on artificial intelligence, with ...
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In the contemporary landscape of technology-driven industries, the integration of artificial intelligence into technology scouting is imperative for enhancing innovation and sustaining competitiveness. This research aims to forge a framework for technology scouting based on artificial intelligence, with a specific focus on technology-based companies. Employing a qualitative approach, data collection utilized the meta-synthesis method devised by Sandelowski and Barroso. This involved a systematic review of 28 articles relevant to the research goal out of a pool of 253 primary articles. The final selection of articles was based on predefined inclusion criteria. The research's validity was confirmed through adherence to criteria, team meetings, expert consultations, and an exhaustive audit for theoretical consensus, while reliability was ascertained through the Critical Evaluation Skills Programme. The framework spans five dimensions: technology scouting tools, technology life cycle, firm environment, firm's approach to the environment, and firm's absorptive capacity. The findings underscore the pivotal role of AI-based technology scouting tools, elucidate the nuanced dynamics of the technology life cycle, and reveal the multifaceted aspects of the enterprise environment. The research outlines strategic approaches for navigating the evolving technology landscape, underscoring the imperative of absorptive capacity for the effective utilization of artificial intelligence technologies. By delivering actionable insights and strategic counsel, this research serves to furnish technology-based companies with a robust underpinning for negotiating the intricate intersection of AI and technology surveillance. In doing so, it propels sustainable growth, fortifies competitive advantage, and fosters enduring innovation.IntroductionIn the dynamic world of technology-driven industry, the role of strategic technology management, particularly in the technology selection and acquisition phases, cannot be overemphasized if success is sought in innovation-driven companies. Focusing on technology-oriented companies that currently face a rapid industrial evolution, the present study highlights the indispensable role of technology scouting, equipped specifically with artificial intelligence (AI), in grappling with the imminent competitive environment. The study proposes a framework that anticipates a future where AI plays a central role in technology acquisition and that strives to enhance absorptive capacities by bridging the adaptation gap. Drawing upon AI, the propsoed framework not only ensures proper technology selection by firms but also drives them toward cutting-edge technological innovations. Serving as a guide for decision-makers, technology strategists, and specialists, the study is expected to contribute, both theoretically and practically, to the understanding and advancement of technology scouting in tech-driven companies. Moreover, it explores and identifies the needs of organizations navigating the intricate technology landscape to derive actionable insights that ensure sustainable innovation leadership.What is the framework for technology scouting based on artificial intelligence in technology-oriented companies? Literature ReviewIn today's rapidly evolving tech landscape, it is essential to cope with the changing business environment (Kujawa and Paetzold, 2019). Ahammad et al. (2021) linked strategic agility to search strategies. Wang and Quan (2021) studied the impact of technology selection uncertainty on firms’ absorptive capacity. Vuorio et al. (2018) explored the significance of competitive edge in tech-driven enterprises. Kerr and Phall (2018) developed a scouting process model. Nasullaev et al. (2020) reiterated the alignment of strategy and tech scouting. Xu et al. (2021) advocated patent analysis in scouting. Sikandar et al. (2021) reiterated patents' innovation measure. Tabrizi et al. (2019) observed a shift to tech-centric business models. Stute et al. (2021) noted the importance of AI in supply chain enhancement. Mariani et al. (2023) classified the motivations underlying AI adoption. Stahl et al. (2023) addressed AI ethics while D'Almeida et al. (2022) categorized AI applications. Wang et al. (2020) identified AI algorithms. Despite these efforts, scant research has been reported on tech transformation, especially AI. This study adopts the meta-synthesis method to explore the digital transformation complexities, focusing on AI's transformative potential and bridging the gaps to derive a roadmap for navigating tech-driven industries. MethodologyEmploying a qualitative approach and the meta-synthesis method, a seven-step process (including goal setting, review, selection, extraction, analysis, quality control, and model development) was meticulously followed to develop an AI-based technological scouting model for advanced tech firms. A systematic search yielded 253 articles, 28 of which met the inclusion criteria and were validated through team meetings, software analysis, and expert consultation. Reliability was ensured since 89% of the articles received excellent scores via the Critical Evaluation Skills Program, indicating high quality. ResultsThe research adopted a classified analysis perspective, utilizing inductive analysis based on Sandelowski and Barroso (2007). This involves extracting primary codes related to AI-based technology observation in high-tech companies, identifying patterns through open coding, and classifying concepts into sub-categories and main categories via axial coding. Table 1Factors Affecting AI-Based Technology ScoutingCategorySubcategoryConceptsTechnology Scouting ToolOpen Source Intelligence (OSINT) ToolsWeb scraping tools, social media monitoring, online forums, patent databases, news aggregators, competitive intelligence tools, and data analytics platforms.Machine Learning and AI ToolsNatural Language Processing (NLP), predictive analytics, pattern recognition, chatbots, sentiment analysis, machine learning, and cognitive computing tools.Collaboration and Communication PlatformsOnline collaboration tools, project management platforms, virtual team collaboration, idea management, crowdsourcing, communication apps, and workflow automation.Technology Life CycleInnovation and InventionIdea generation, R&D, concept testing, prototyping, patenting, technology transfer, proof of concept, funding, collaborative research, and feasibility studies.Technology Adoption and DiffusionTechnology readiness, market analysis, adoption theories, market penetration, standardization, compliance, user testing, and overcoming adoption barriers.Technology Evolution and ObsolescenceContinuous improvement, iterative development, versioning, obsolescence management, legacy systems, discontinuation planning, sustainability, disruptive tech, and sunset planning.Company EnvironmentCompetitive Landscape AnalysisCompetitor mapping, SWOT analysis, industry benchmarking, market share analysis, competitive intelligence, PESTLE analysis, collaboration strategies, positioning, and sustainable advantage.Regulatory and Legal EnvironmentIntellectual property management, standards compliance, regulatory impact, patent landscape analysis, legal risk, data protection, ethics, antitrust, government policies, and international regulations.Internal Organizational EnvironmentCulture, cross-functional collaboration, governance, change management, talent, agile structures, infrastructure, decision-making, metrics, and employee engagement.The Company's Approach in Facing the EnvironmentInnovation Strategy FormulationRoadmapping, open innovation, blue ocean strategy, core competency analysis, innovation ecosystems, portfolio management, ambidextrous approach, horizon scanning, lean methodologies, and design thinking.Adaptive and Resilient PracticesCrisis management, scenario planning, risk management, agile project management, supply chain resilience, continuous learning, adaptive capabilities, technology portfolio flexibility, and fostering innovation culture.Strategic Alliances and PartnershipsCollaborative innovation, joint ventures, technology ecosystems, university-industry collaborations, innovation networks, open source, licensing, technology transfer, competition, and strategic partnerships.Absorption Capacity of the CompanyLearning and Knowledge ManagementOrganizational learning, knowledge creation, sharing platforms, communities of practice, intellectual capital, training programs, technology scouting, learning culture, and tacit knowledge transfer.Resource Allocation and UtilizationTechnology budgeting, allocation models, ROI analysis, portfolio management, cross-functional sharing, resource efficiency, project prioritization, dynamic reallocation, innovation finance, and risk management.Adoption of Emerging TechnologiesScanning trends, piloting new tech, foresight methodologies, early adoption, readiness assessments, and collaborative ecosystems for adoption, mitigating risks, cross-functional teams, integration, and continuous monitoring. DiscussionTo address the crucial gap in technology scouting in technology-oriented companies involved in the joint AI and technology scouting, the study develops a framework of five dimensions. Open-source smart tools and machine learning are explored as essential components of the "Technology Scouting Tool"dimension to contribute to the development of a cohesive strategy. The "Technology Life Cycle" dimension guides the firm through the innovation, adoption, and evolution stages. The "Company Environment" dimension adopts a multifaceted approach, considering competitive analysis, regulatory factors, and internal dynamics. The strategic components of the "Firm's Approach to the Environment" underline the contributions of innovation strategy, adaptability, and alliances while "Firm's Absorptive Capacity" offers practical insights by underscoring learning, resource allocation, and technology adoption. ConclusionThe proposed framework provides a strategy tailored for tech-oriented firms incorporating AI into scouting and offers strategic insights across the five dimensions to tackle nuanced challenges in the technology landscape. Advocating advanced open-source tools and strategic approaches, it explores the technology life cycle, considers diverse aspects of firm environment, and launches an AI-driven future. Acknowledging limitations and emphasizing proper deployment of AI, the study lays the foundations for future studies to validate and expand the framework while ensuring responsive and sustainable application of AI-based surveillance technologies in corporate contexts. Keywords: Artificial intelligence, Technology scouting, Technology-oriented companies, Digital transformation.
Data science, intelligence and future analysis
Seyed Mohammad Mahmoudi; Mohammad Jafari; mahsa Pishdar
Abstract
Artificial intelligence provides unique opportunities to improve the performance of various industries, including the automotive industry. The present study seeks to identify the applications and requirements of using artificial intelligence in new automotive products such as self-driving cars ...
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Artificial intelligence provides unique opportunities to improve the performance of various industries, including the automotive industry. The present study seeks to identify the applications and requirements of using artificial intelligence in new automotive products such as self-driving cars by obtaining opinions from managers and employees of domestic automotive companies through semi-structured interviews and thematic analysis. The interviewees included 11 managers and 17 employees, of which 15 had a bachelor's degree, 11 had a master's degree, and 2 had a doctorate degree. 21 codes were identified in the applications section and 26 codes were identified in the requirements section. After conducting 28 interviews, theoretical saturation was achieved. From the codes identified in the applications section, self-driving cars and voice assistants, shared transportation, and resource allocation, expert staff, and team formation can be mentioned in the requirements section. Considering the variety of artificial intelligence applications in new car products and according to the specified requirements according to the opinions of experts, the development of a suitable platform for hard and soft technologies in an integrated manner; And government support regarding the creation of legal infrastructure can improve the development path of the current technology. Of course, in order to create a context for the successful operation of artificial intelligence in the automotive industry, all the effects of its application from different cultural and social aspects should be considered with a systematic perspective.
Introduction
Artificial intelligence has enormous potential to reduce the problems of automakers around the world. Nevertheless, reports show that between 2017 and 2019, the number of automobile manufacturers that consciously refrained from using artificial intelligence and related technologies such as machine learning and neural networks in the production and supply of new products such as connected and autonomous cars have done so; it has only increased from 26% to 39% (Gandhi et al., 2022).
The lack of attention to the complexities of artificial intelligence and the acceleration of the use of this technological tool have caused the failure of automobile manufacturers' plans to provide intelligent products (Fernandes et al., 2022). Despite the applications and benefits of artificial intelligence in automotive services, there are still many ambiguous aspects regarding the use cases and prerequisites that different researches have addressed from a specific perspective, and the lack of a framework consistency in this area is felt. For example, Gupta and colleagues (2021) argue in their research that cars equipped with artificial intelligence technology are not capable of evaluating and classifying their environment on their own.
The present study aims to identify applications and requirements related to the use of artificial intelligence in new automotive products, such as self-driving cars. Therefore, the results of this study can be useful to automobile manufacturers trying to revitalize the potential and improve their products in the field of using artificial intelligence.
Research Question(s)
In this regard, in order to achieve the objectives of the research, a fundamental question is posed:
“What are the requirements and prerequisites for using artificial intelligence in the delivery of new products such as autonomous and connected cars"?
Literature Review
The applications of artificial intelligence in automotive products can be divided into two categories: personal applications and social applications. Personal applications refer to products designed with two elements of security and convenience for users in mind. These applications include cruise control, automatic parking, voice assistant, alert systems, and route suggestion systems, all of which manifest in self-driving cars (Paliotto et al., 2022). Social applications refer to products whose effects include all members of society. For example, self-driving cars and cars equipped with artificial intelligence will reduce urban congestion or reduce the need for parking. These cars also play an effective role in transporting disabled and vulnerable people. Other social applications include the role of these cars in reducing environmental pollution and shared transportation (Zhang et al).
Regarding the requirements and prerequisites for the use of artificial intelligence in modern automotive products, various researches have been carried out, among which we will cite only a few examples below:
- Barzegar and Elham (2019), using a descriptive-analytical approach, the criminal liability of the user of self-driving cars in accidents was discussed.
- Demlehner et al. (2021) conducted a study to identify 20 applications of artificial intelligence in the production of intelligent and autonomous cars and to examine these applications from the two dimensions of business value and realizability.
- Othman (2022) studied the requirements for the use of artificial intelligence in automotive products, such as cruise control, warning systems and self-driving cars, and studied its consequences from the point of view security, the economy and society, etc.
Methodology
This research is”an applied research”in terms of purpose and a descriptive survey in terms of data collection. The information collection method is a survey and semi-structured interview with experts. The experts include two categories of managers and senior employees from the research and development department of interior automakers who have more than five years of work experience and are familiar with artificial intelligence. In order to collect samples, semi-structured interviews were conducted with the target people in person or in person using the snowball method.
The method of data analysis in this research is thematic analysis; so, after implementing the text of the interviews and analyzing and coding it with the thematic analysis method, 21 codes were identified in the applications section and 26 codes were identified in the requirements section. After carrying out 28 interviews, theoretical saturation was reached. From the codes identified in the applications section we can refer to self-driving cars, voice assistant, and in the requirements section we can refer to resource allocation, specialized personnel.
Results
The main goal of this research was to identify the applications and requirements related to the use of artificial intelligence in new car products, such as self-driving cars. According to the review and analysis of the interviews with the thematic analysis method, the research results were determined into two groups:
In the first group, applications of artificial intelligence in new products of automobile manufacturers were identified, such as self-driving cars, cruise control and warning systems, among which, according to the interviews, self-driving cars were the most important. Therefore, in this research, emphasis was placed on identifying key applications, which were separated into two dimensions: personal and social applications; In this regard, a total of 21 applications were identified.
In the second group, the requirements and prerequisites of artificial intelligence were classified, and due to the dispersion of results in previous research, a great effort was made to integrate the requirements. In this regard, the requirements of artificial intelligence are divided into six general categories, which are: 1- road infrastructure, 2- technical infrastructure and equipment, 3- knowledge, 4- users, 5- the role of managers, 6- culture, Rules. Therefore, as far as possible, in this category, fundamental requirements such as society, individual, technology and knowledge have been taken into account.
In short, taking into account the diversity of applications of artificial intelligence in modern automotive products, it can be concluded that, according to the established requirements and opinions of experts, the development of a suitable and integrated platform of hard technologies and soft law requires serious support from the government and attention to the creation of legal infrastructure. Therefore, we suggest that policy makers and managers of the automobile industry, in order to facilitate the technological development and optimal use, and successful application of artificial intelligence in the automobile industry, should all first systematize their point of view, and pay particular attention to the necessary infrastructure and consider different dimensions such as technical, cultural, social, etc.
Keywords: Artificial intelligence, applications and requirements, new products, self-driving cars..
Data science, intelligence and future analysis
Mohammad Hoseini Moghadam
Abstract
The touch of the product plays an important role in the final decision of the customer when purchasing from physical and online retail, and the sensations that come to be enjoyed through touch enable them to experience the product from all angles. Therefore, considering the importance of touch, ...
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The touch of the product plays an important role in the final decision of the customer when purchasing from physical and online retail, and the sensations that come to be enjoyed through touch enable them to experience the product from all angles. Therefore, considering the importance of touch, this research has investigated the lived experience of touching the product from the point of view of customers of physical and online stores. The following article is done with qualitative method and phenomenological paradigm. The research community is made up of electronic and clothing buyers from online and physical stores: Technolife, Adak, Havadar and Happyland in Tehran, and through semi-structured interviews, evidence was collected based on the purposeful sampling method. The interviews continued until reaching the theoretical saturation, and in this research, the interviews reached saturation with 15 people. Based on the extracted results, the main themes include; Product perception is physical touch, virtual touch, touch experiences, need for touch and touch perceptions. According to the results, managers of physical and online stores should provide conditions (such as the use of modern technologies) that touch and contact with the product happen to both groups of online and physical buyers so that they can buy products based on their needs and wants, and also this research can pave the way for the development of touch literature for researchers.IntroductionThroughout human history, the idea of progress has been a central concern for thinkers and intellectuals, with technological advancements playing a pivotal role in shaping the development of societies (Du Pisani, 2006; Rivers, 2002). Artificial intelligence (AI), as a driving force behind the fourth industrial revolution, has had a profound impact on numerous fields, including scientific research and discovery (Velarde, 2020). AI has revolutionized scientific knowledge to such an extent that distinguishing between the discoveries made by intelligent machines and human experts has become increasingly difficult (Krenn et al, 2022). This article explores the implications of AI for the future of scientific progress and its potential to give rise to post-normal science.Here is my attempt at rewriting the text as a senior researcher:The central question examined in this article is: what role does AI play in shaping the future of scientific developments? In exploring this overarching question, several related questions are also considered: How can AI be leveraged to uncover and obtain new scientific knowledge? Can novel computing techniques based on AI not only detect unusual patterns and events in data, but also lay the groundwork for new scientific advances? Might AI furnish new theories and transform our comprehension of science? Can AI-based scientific systems determine which scientific questions are worthwhile, and for whom are they valuable? Looking ahead, what assurances will scientists have about the validity of AI-based analyses in science?In response to these pressing questions, the core hypothesis presented is that AI has become the foundation for the emergence of a new breed and style of scientific discovery, which can be characterized as post-normal science. To evaluate this hypothesis, the historical background of relevant research is reviewed. AI represents a seismic shift in the practice of science, enabling analyses and discoveries that would be impossible for humans alone. While promising, it also poses troubling philosophical questions about the nature of truth and scientific understanding.MethodologyA variety of research methods were employed to address the questions raised in this study, including a systematic review of relevant literature to identify the transition from normal to post-normal science, trend analysis to examine the influence and expansion of AI in scientific discoveries, documentary studies to obtain theoretical and conceptual foundations, and modeling to understand and describe the progress of post-normal science under the influence of AI.FindingsAI has facilitated a new model of scientific discovery, known as data-driven scientific discovery, which derives hypotheses from data rather than relying on preconceived assumptions (Wheeler, 2004). This approach has transformed traditional sciences into data sciences, with scientific patterns extracted from data and an increasing focus on intelligent automation in scientific progress (King & Roberts, 2018). As a result, a new type of epistemology has emerged, characterized by the involvement of machines in scientific discovery and the advancement of the science cycle. This development, referred to as "Science 0.4" or the fourth type of science, has integrated science into society, enabling every citizen to participate as a scientist and fostering a shift towards "open science" (Odman & Govender, 2021).AI's impact on scientific research has been guided by several key principles, including sustainability, different forms of knowledge, accountability and responsibility, values and interests, collective wisdom and rationality, and non-determinism and non-linearity in the process of scientific discovery. AI has contributed to the realization of post-normal science by facilitating simulation and modeling, improving decision-making, promoting ethics, embracing diversity, fostering interdisciplinary collaboration, expanding stakeholder engagement, and enabling big data analysis.ConclusionAI systems have fostered interdisciplinary collaborations and facilitated the integration of knowledge and expertise across various fields, allowing for the identification and resolution of complex, interdisciplinary scientific issues. This collaboration disrupts the linear progression of normal science, promoting a more integrated and cooperative approach to problem-solving. Furthermore, AI has introduced new ethical and social considerations in scientific research, necessitating a departure from conventional forms of normal science. Although it remains uncertain whether AI will replace the human role in scientific discovery, it is clear that scientists and institutions that embrace AI technology will surpass those that do not.RecommendationsTo achieve excellence in the field of AI within scientific institutions, it is crucial to understand the "state of maturity in AI" and to establish a starting point for the governance system of science and its actors. In this process, scientific institutions can be categorized along a spectrum, ranging from those seeking to familiarize themselves with AI-driven changes in scientific discovery to those actively leveraging AI technology to advance scientific knowledge.Keywords: Artificial Intelligence, Normal Science, Post Normal Science, Science Progress, Scientific Discoveries.
Ehram Safari; Ali Asghar Ansari
Abstract
One of the most important issues in the development of artificial intelligence is the adoption of the use of artificial intelligence by the private and public sectors. In other words, in order for artificial intelligence to be used in a country or industry, it is necessary to identify and evaluate the ...
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One of the most important issues in the development of artificial intelligence is the adoption of the use of artificial intelligence by the private and public sectors. In other words, in order for artificial intelligence to be used in a country or industry, it is necessary to identify and evaluate the important factors of adoption. The purpose of this study is to identify and rank the factors affecting admission in the public and private sectors in Iran. For this purpose, first, a set of models and factors affecting the adoption of technology were extracted from the literature and opinions of experts and were classified into three categories: technological, organizational and environmental factors Then, the most important factors in each category were determined through a collection questionnaire, and using nonparametric Friedman test for each category with the most important and least important criteria. In order to weight and prioritize the factors, the quantitative approach and BWM technique have been used. The statistical population of the study included 37 experts in artificial intelligence in the public sector and 45 experts in the private sector. According to the obtained results, in the public sector, 3 important factors of admission are the support of senior managers, the existence of the required infrastructure for artificial intelligence and the existence of specialized and capable forces in the field of artificial intelligence. Efficiency and productivity with the use of artificial intelligence, cost savings with the use of artificial intelligence and ease of use and learning has been easy.
yazdan shirmohammadi; Arash Bostan manesh
Abstract
Using artificial intelligence technology, smart stores transfer a lot of customer and product information (big data) including facial recognition, smart sensors, smart shelves, automatic payment and interactive displays at high speed based on the fifth generation (5G) internet. Since the spread of the ...
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Using artificial intelligence technology, smart stores transfer a lot of customer and product information (big data) including facial recognition, smart sensors, smart shelves, automatic payment and interactive displays at high speed based on the fifth generation (5G) internet. Since the spread of the corona virus has changed the way of life and business today, that's why marketers have used new strategies based on artificial intelligence to advance. This research analyzed the hedonic factors of customers' purchases based on the Hedonic Information Systems Acceptance Model (HISAM). The sampling method of this research was simple random and its number was 404 people. The measurement tool in this research is a questionnaire. Statistical analysis was done using structural equation method and using SPSS and Amos software. To determine the causal relationship between the variables using the structural equation model method and significance levels in order to test the hypotheses, a p_value smaller than 0. 05 was considered. The results of this research showed that the perceived ease of use, perceived benefit and perceived enjoyment have a positive and significant effect on the purchase intention due to the technology readiness of customers. Also, the results of the research indicated that the mediating variable of technology readiness was effective from optimism, innovation, discomfort and insecurity, and perceived ease of use, perceived enjoyment, and perceived benefit had a positive effect on customers' purchase intentions from smart stores in the era of Corona.
Reyhaneh Forouzandeh Joonaghani; mirali Seyednaghavi; Vajhollah ghorbanizadeh; Mohammad Taghi Taghavifard
Abstract
In recent years, the application of artificial intelligence, especially machine learning, has grown significantly in the field of HRM, which is unknown to many managers and experts in the field of HR due to the newness of this field. A lot of data is being generated by users of organization in HRM domains ...
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In recent years, the application of artificial intelligence, especially machine learning, has grown significantly in the field of HRM, which is unknown to many managers and experts in the field of HR due to the newness of this field. A lot of data is being generated by users of organization in HRM domains and the related fields, which are difficult to analyze and use in HR activities. The capabilities of data science and machine learning have been able to make great contributions to the field of HRM and beyond to the management of the organization with descriptive, diagnostic, predictive and prescriptive reports and analyses. The purpose of the research is to examine the measures that have been taken so far in the field of HRM intelligence, and in this research, three main questions are answered. The first question is to identify HRM activities that can be made intelligent. In the second question, the application of various ML algorithms in HRMI has been identified. In the third question, based on the maturity levels of data analytics, the classification of "ML algorithms in intelligent HRM functions" has been made. In order to answer , a wide range of articles were extracted from reliable scientific databases and journals and analyzed based on a mixed method. In this method, qualitative and quantitative methods for data analysis were investigated at the same time. IN the quantitative part, text mining algorithms were used Python language, and in the qualitative part, thematic analysis was used MAXQDA2020 .
Maghsoud Amiri; Iman Raeesi Vanani; Seyed Hossein Razavi Hajiagha; Taranoush Jafari
Abstract
Proper management and optimal allocation of financial resources will increase gross national product and growth, create jobs and increase public welfare. The purpose of this study is to present an investment strategy that has tried to pave the way for the development of the investing company in the financial ...
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Proper management and optimal allocation of financial resources will increase gross national product and growth, create jobs and increase public welfare. The purpose of this study is to present an investment strategy that has tried to pave the way for the development of the investing company in the financial markets. Therefore, the forthcoming research can be considered as applied in terms of purpose. Also, considering that in the present research, mathematical modeling, modeling, artificial intelligence, etc. are used and the optimization of the investor company's portfolio is evaluated with the proposed model, so it is a quantitative and descriptive research. This study evaluated the performance of the proposed model in three modes: prudent, moderate and risky investor company. The results showed that for all three cases, the proposed strategy performs significantly better than the market index and other previous strategies. At the end of the investment period, the risky portfolio was more valuable than other portfolios. On the other hand, a prudent portfolio has achieved a more stable and stable return. These results revealed that the proposed fuzzy programming is able to reflect the characteristics and desires of the investor company in the portfolio composition.
