عباس خمسه

Today, one of the requirements and foremost policies in research organizations is preparation for commercialization of research achievements. Commercialization is a multi-dimensional activity that requires a systematic approach in which stakeholders, functions, environmental conditions and other influencing factors in the commercialization process are considered. In addition to producing, transferring and promoting knowledge, agricultural research institutes are responsible for the mission of converting and exploiting knowledge, or commercialization. According to the relatively new approach of universities and domestic research institutes to the commercialization category, in many cases there is no clear understanding of the importance and necessity of commercialization among those engaged in agricultural research, and so far the process, organizational and organizational structure, and coherent executive mechanisms in the form of a model The requirement for the commercialization of agricultural research has not been defined. Based on this, the current article identifies the indicators and requirements affecting the commercialization ecosystem of agricultural research achievements. This research has been conducted through Sandelowski and Barso's metacombination method with a systematic review of 36 articles related to the purpose of the research out of 325 primary articles. The validity of the research was confirmed according to the entry criteria of holding meetings with the members of the research team, using an expert and auditing the entire process for theoretical consensus. Reliability was also confirmed through the critical evaluation skills program. The agricultural research commercialization ecosystem can be classified into three categories (willingness and ability to commercialize, commercialization leadership, and innovation and marketing).

Process innovation is a subset of innovation related to improving or reconfiguring production methods, workflows, or techniques. Process innovation aims to optimize the production or delivery method of existing goods or services. The importance of process innovation lies in its potential to simplify operations, reduce costs, minimize resource consumption, and improve productivity and quality. In agriculture, innovation catalyzes transformation, efficiency improvement, sustainability, and flexibility in production systems. Process innovation in sugarcane production and processing is vital to address the multifaceted challenges facing the industry, including market demand fluctuations, environmental degradation, and socio-economic inequalities. By embracing innovation, stakeholders can increase productivity, optimize resource use, reduce environmental impacts, and as a result, ensure the long-term sustainability and resilience of the sugarcane sector. In this regard, the present research was designed and carried out to explain the indigenous components of sugarcane production and processing with an emphasis on process innovations.

The research adopted Sandelowski and Barso's meta-composite qualitative approach. The process started with a systematic review of 405 primary articles and finally, 33 articles related to the research purpose were selected. The inclusion criterion was the publication in the field of sugarcane production and processing between 2010 and 2023. To validate the proposed model and increase acceptability, the fuzzy Delphi method with a panel of experts was used. The results were verified and reviewed by the experts.

The fuzzy Delphi resulted in deriving 57 key local indexes within four processes, i.e. planting, growing, harvesting, and processing. According to the results, it can be concluded that the integration of indigenous knowledge systems with modern innovations is important to enhance sustainability, flexibility, and learning in the sugarcane industry. In addition, joint efforts among the beneficiaries are needed to reinforce innovation, promote knowledge exchange, and develop comprehensive strategies to advance the sustainability and prosperity of the sugarcane sector.

Conclusion/ Implications: 

It can be inferred that innovation in the process of sugarcane production is too important to be ignored because it acts as a catalyst to enhance productivity, sustainability, and flexibility in the industry. Hence, it is suggested to create an interdisciplinary platform for cooperation and knowledge exchange to integrate indigenous knowledge systems with modern innovations in the process of sugarcane production. This way, communication among researchers, policymakers, industry players, and local communities is facilitated to create and implement innovative solutions proportional to specific fields and needs of sugarcane producers.

Efficient portfolio management of research and development projects is paramount for steering innovation and sustainability within the healthcare sector. Nonetheless, despite its significance, there exists a research gap in examining the dimensions and components influencing the portfolio management of sustainable research and development projects in healthcare services. The present study aims to bridge this gap by identifying and analyzing the key dimensions and components affecting the portfolio management of sustainable healthcare research and development projects. Employing a qualitative approach, data collection was conducted using Sandelowski and Barso's metacomposite method, involving a systematic review of 32 relevant articles from a pool of 328 primary sources. Rigorous validation procedures, including criteria-based assessment, team meetings, expert input, and theoretical consensus auditing, confirmed the validity and reliability of the research. The findings delineated 36 components across 7 main dimensions, encompassing environmental impact assessment, resource optimization, economic sustainability, social responsibility, stakeholder engagement, regulatory compliance, and long-term impact assessment. The insights garnered from this research offer valuable guidance for policymakers, healthcare practitioners, researchers, and stakeholders involved in planning and executing sustainable research and development projects. Leveraging this knowledge stands to enhance resource allocation efficiency, foster collaboration, and ultimately drive forward innovation and sustainability in the healthcare domain.

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.

Due to the direction of trends toward 5.0 industry technologies, a new concept called digital transformation has emerged, which has affected various areas of society. Since reaching the desired level of application of Industry 5.0 requires the training of skilled human resources in the application and development of technologies in this field, its widespread use in education was quickly followed in many countries. The innovative integration of these technologies in education has made the traditional methods of education extinct, which has led to a serious transformation in the learning process in schools and universities. Identifying the dimensions and components of digital transformation in education can help policymakers facilitate this process and lead to accelerating the achievement of digitalization goals. Intending to identify these dimensions and components, this research has examined 47 related articles with the meta synthesis method. Then, in the quantitative section, to determine the order of importance of the dimensions and their weighting factor, a ranking has been done. The results of the research showed that among the six identified dimensions, organizational transformation with a weighting factor of 0.217 has the highest priority and infrastructural transformation with a weighting factor of 0.201 has the second priority. The general results of the research have also shown the importance of adopting an ecosystem approach in the implementation of digital transformation in schools

The emergence of industry 4.0 and the globalization of the supply chain has caused the supply chains to be more exposed to crises and disruptions. One of the concepts that can be a solution in this regard is resilience and how to design and implement strategies related to it. In this regard, emerging technologies Industry 4.0 can lead the way. The purpose of this applied and mixed exploratory research is to examine the dimensions of supply chain resilience with a focus on technology in sharing economy businesses.

The participants of the qualitative part of the research were 13 people from sharing economy business experts who were selected by the snowball method. The statistical population of the quantitative part was also 71 managers and experts who were selected in a purposeful and accessible way. In this research, data have been collected with the library method by reviewing articles, books and theses, and then a researcher-made questionnaire. in the qualitative part, using meta-synthesis and Delphi method with Excel and SPSS software, and in the quantitative part, data has been analyzed using confirmatory factor analysis with the method of partial least squares and Smart-PLS software.

Findings of this research include 64 extracted codes that are grouped into 4 factors. Organizational-cultural factor with 14 indicators, environmental-market factor with 17 indicators, technology factor with 13 indicators and supply chain factor with 20 indicators have been obtained. According to the results of the research, the supply chain factor has the greatest impact on the resilience of the supply chain, followed by the cultural-organizational factor and the technology factor, and finally, the environmental-market factor has the least impact. According to these results, it is suggested that increase coordination and cooperation among supply chain factors.

This research aims to develop a robust predictive model for evaluating the strategic cooperation between Iran's banking system and fintech startups. Leveraging insights from 14 experts within the banking and fintech sectors, a hybrid methodology involving the foundation's interview tool and data analysis was employed. Thirty-one indicators, categorized into six key factors influencing strategic cooperation, were identified. Using a fuzzy approach and MATLAB software, a conceptual model was crafted to assess the strategic cooperation of the banking system with fintech startups. Input from 320 industry professionals and managers further enriched the analysis. The findings underscore the pivotal dimensions shaping this cooperation, including barriers to entry, external factors, explanatory elements, varying cooperation levels, consequences of collaboration, and the motivations driving banks and fintechs. The level of strategic cooperation was determined to be in the medium to high range. Notably, the Anfis-designed model exhibited acceptable validity and predictive power. This study contributes not only by unraveling critical cooperation dimensions but also by furnishing a reliable predictive tool. The comprehensive approach, amalgamating expert insights, diverse indicators, and advanced analytical tools offers valuable insights to fortify strategic cooperation in the banking-fintech nexus.

In the era of Industry 5.0, successful technology transfer has become a strategic necessity for organizations. The integration of advanced artificial intelligence with human expertise and creativity offers new opportunities for innovation and value creation, while also presenting new challenges. This research aims to examine how artificial intelligence and human factors influence the technology transfer process in the Industry 5.0 era. Using a combined approach of meta-synthesis and qualitative content analysis, 32 relevant articles were reviewed. The validity of the research was confirmed using the Sandelowski and Barroso (2007) method, and its reliability was determined through the Critical Appraisal Skills Program (2018). The research findings led to the identification of 4 main dimensions, 12 components, and 36 indicators, which include: improvement of technology transfer processes, enhancement of human capabilities, improvement of human-machine interaction, and knowledge and innovation management. Based on these findings, it is recommended that organizations focus on investing in the development of advanced AI systems, training and empowering human resources, improving human-machine interaction, and creating integrated knowledge management systems. This research provides a comprehensive framework for understanding and managing technology transfer in the Industry 5.0 era and emphasizes the importance of an integrated and balanced approach in combining AI capabilities with human competencies.

The aim of the current research is to provide a model of factors affecting the implementation of green supply chain management in the construction industry. This research was conducted using a mixed approach. The current research design is the analysis of hidden content and the target community, managers and contractors active in the construction industry and familiar with the supply chain of this industry, and the purposeful sampling method was used. For modeling, the structural-interpretive method was used, and finally, the power of penetration and the degree of dependence of the factors were determined by using Mick-Mac analysis. In order to identify the components of the research, first, a meta-combination approach and a systematic search of sources were used in the period between 1392 and 1402 for internal research and between 2013 and 2023 for external research, and then semi-structured interviews with 14 experts were used. Based on the findings of the research, 101 primary codes were extracted in 14 categories including: internal environmental management, green technology, reverse logistics, green purchasing, green design, green construction, green transportation, green human resources management, green marketing management, internal and external obstacles and drivers. and green supply chain management performance were classified. Also, the modeling results have shown a five-level model in which internal and external drivers and obstacles were placed as the most influential variables in the fifth level and the green supply chain management performance variable as the most influential variable in the first level.

The purpose of this research is to identify the influential components in the use of new technologies for the media industry with a focus on digital transformation.

To achieve the research objective, all studies conducted between the years 2000 and 2022 on the research topic were examined using the meta-synthesis method based on the seven-stage approach of Sandelowski and Barroso (2007). By searching for keywords in domestic and foreign scientific databases, a total of 1753 sources were identified. The identified sources underwent multiple stages of screening, including examining publication dates and assessing compatibility of titles, abstracts, and full texts with the study objectives. Finally, 42 articles were selected as the final articles for the meta-synthesis.

Finally, eight effective components of big data infrastructure, digital literacy of managers and employees, innovation in technology and content, technological capabilities of new media, media communication management, semantics in the media industry, audience participation, and strategy Technology-based businesses were identified.

Deliberating on these components and paying attention and applying concepts related to them, especially paying attention to the dimensions of big data infrastructure in the space of digital transformation and innovation in technologies and digital content can be a guide and instruction for any media that It plans to implement the approach of digital transformation in its media organization through new media technologies.

This study adopted a qualitative approach to investigate the obstacles faced by Small and Medium-sized Enterprises (SMEs) in financing for their production activities. Through the utilization of the meta-synthesis method and a comprehensive review of 23 relevant articles, this research identified 57 factors categorized into 10 key dimensions that acted as barriers to financing. These dimensions encompassed aspects, such as financial infrastructure and accessibility, legal framework and political implications, information asymmetry and transparency, collateral requirements, asset-based financing, loan terms and conditions, credit assessment and risk perception, market dynamics and competition, technology advancements and digitalization, social and economic factors, regional inequalities, financing mechanisms, and innovations. By presenting a structured framework, this research aimed to enhance the comprehension of these challenges and provide potential solutions. 

In today's intricate global economic landscape, Small and Medium-sized Enterprises (SMEs) represent thriving sectors characterized by their innovation, dynamism, and growth potential. These enterprises often hailed as the backbone of economies worldwide play a pivotal role in driving economic development, generating employment opportunities, and fostering technological advancements. However, despite their significant contributions, SMEs encounter a myriad of formidable challenges that impede their prosperity and hinder their optimal contribution to economic progress. From legal obligations and market competition to resource constraints and technological barriers, these businesses navigate a complex environment fraught with obstacles. Among the various hurdles they face, one of the most prominent one pertains to production financing, wherein SMEs encounter difficulties in accessing capital and securing adequate financial resources. Such barriers manifest in the form of stringent lending criteria imposed by financial institutions, information asymmetry and lack of transparency, restrictions on collateral, and exorbitant transaction costs. Consequently, these enterprises grapple with limited budgetary options, thereby curtailing their growth prospects and impeding their capacity to innovate and compete in the market. This study endeavored to bridge the existing research gaps by conducting an in-depth examination of the barriers faced by SMEs in securing financing for production activities. The primary objective was to develop a comprehensive model for analyzing production financing barriers in small and medium-sized enterprises. Such a model can serve as a valuable tool for policymakers, financial institutions, industry associations, and entrepreneurs alike, empowering them to formulate effective strategies aimed at overcoming these obstacles.

This study adopted a qualitative approach following the methodology proposed by Sandelowski and Barso (2007) for data collection. The methodology consisted of 7 steps, which involved formulating research questions, conducting a systematic literature review, selecting relevant studies, extracting information from the chosen studies, analyzing and synthesizing the findings, ensuring quality control, and presenting the outcomes. To conduct a comprehensive search, reliable databases were systematically queried using relevant keywords and phrases, such as "small and medium businesses", "production financing", "financing barriers", and related terms. The search covered titles, abstracts, and keywords. Inclusion criteria were applied, including the selection of non-Persian qualitative papers published between 2014 and 2023 indexed in ISI or Scopus, as well as valid Persian articles published between 2013 and 2014. Initially, 282 articles were identified and after a rigorous selection process, 23 articles were chosen for the final analysis. To ensure validity, entry criteria, team meetings, expert opinions, and EndNote software were employed. Reliability was determined by critically evaluating and scoring the articles. Parameters, such as authors, publication year, research purpose, methodology, and findings, were considered during the evaluation process.

Culmination of the meta-synthesis section yielded an analysis model illustrating the barriers to production financing in small and medium-sized businesses. Being presented in Figure 1, this model encompassed the key findings of the research.

This study explored the financing challenges faced by small and medium-sized enterprises using a hybrid approach and identified 57 indicators across 10 key dimensions that acted as barriers to financing. The dimension of "financial infrastructure and access" encompassed challenges related to accessing financial institutions, banking services, and venture capital. Additionally, the study examined microfinance as an alternative avenue with its own possibilities and challenges. In the dimension of "legal environment and political consequences", barriers, such as legal transparency, compliance, administrative ease, and government support programs were discussed as they impacted the performance of SMEs. The dimension of "information asymmetry and transparency" focused on challenges related to accessing information, transparency in lending practices, and disclosure requirements. The dimension of "collateral requirements and asset-based financing" addressed the complexities and challenges of evaluating and accepting collateral from SMEs, including collateral policies, asset valuation methods, loan-to-value ratios, and non-traditional collateral acceptance criteria, which often posed difficulties for businesses with limited assets. The dimension of "loan terms and conditions" highlighted the challenges that SMEs encountered when seeking financing, including issues related to interest rates, loan duration, flexibility of repayment terms, and collateral requirements. The dimension of "credit assessment and risk understanding" encompassed challenges, such as credit score assessment, financial statement analysis, business viability assessment, and understanding market and economic conditions. The dimension of "market dynamics and competition" explored the diverse effects of market dynamics on the access of SMEs to financing, including factors, such as market structure, competitive landscape, industry concentration, market entry barriers, and impact of new technologies. The dimension of "technology developments and digitization" examined how technology and digital advancements were reshaping the financing landscape for SMEs, including advancements in digital infrastructure, adoption of new financial technologies, and cybersecurity measures. The dimension of "economic and social factors and regional inequalities" addressed various challenges, including levels of economic development, income inequality, access to education, and infrastructure development. Lastly, the dimension of "alternative financing mechanisms and innovations" explored diverse avenues for SMEs to obtain financing. Peer-to-peer lending, crowdfunding platforms, and other innovative financing approaches provided these companies with new opportunities to raise capital and offer more flexible terms. In conclusion, this research shed light on the multifaceted barriers that SMEs faced in accessing financing. By comprehensively examining these dimensions, policymakers, financial institutions, and entrepreneurs could develop effective strategies to overcome these challenges and foster a more conducive environment for SMEs to thrive and contribute to economic growth.
This study proposed recommendations for addressing the barriers that hindered the access of small and medium-sized businesses to production financing. The key recommendations included enhancing transparency and access to financial information, simplifying laws and improving tax transparency, implementing financial education programs to enhance financial literacy, fostering collaboration among the government, financial institutions, and industries to develop innovative financing solutions, investing in digital infrastructure and cybersecurity measures, and implementing supportive policies to reduce regional disparities. It is important to acknowledge the limitations of this research, such as its reliance on the existing literature, which may have implications for the quality and comprehensiveness of the studies reviewed. To gain a deeper understanding of financing trends and dynamics and devise appropriate interventions, it is recommended to undertake longitudinal, comparative, exploratory, and interdisciplinary research.

The international transfer of high technologies plays a pivotal role in the transformation of industries and the transition to Industry 5.0 - a paradigm emphasizing human-centric, sustainable, and resilient industrial development. However, this process faces numerous challenges and complexities, necessitating a profound understanding of its key variables and concepts. The present research aimed to identify and analyze these variables in the realm of high technology transfer in Industry 5.0. Following a systematic literature review protocol, 84 relevant articles published between 2017 and 2024 were selected based on predefined criteria including relevance to the research topic, publication quality, and citation impact. These articles were analyzed using a comprehensive text mining approach incorporating keyword extraction, sentiment analysis, topic modeling, and concept clustering techniques implemented through Python libraries including NLTK, SpaCy, TextBlob, and Scikit-learn. The results categorize the key variables and concepts into five main clusters: high technologies (including AI, IoT, and robotics), technology transfer mechanisms, Industry 5.0 characteristics, implementation challenges (such as cybersecurity risks and high adoption costs) and opportunities (including increased productivity and innovation potential), and regulatory frameworks. These findings unveil various aspects of the technology transfer process, providing insights for stakeholders while highlighting the critical role of human-technology collaboration in Industry 5.0. The study's limitations include potential bias from focusing primarily on English-language literature and the inherent constraints of computational text analysis in capturing context-dependent nuances. This research contributes to a deeper understanding of technology transfer dynamics in Industry 5.0, offering practical implications for policymaking and implementation strategies.

One of the most important factors for the success of a startup is its valuation. At the same time, due to the transition to a new type of efficient economy based on sustainable resources, the role of green technology is increasingly progressing. This study aims to investigate the dimensions affecting the valuation of startups, with a focus on the valuation of green technology. The statistical population consisted of 12 experts related to the field of startups, selected through purposive sampling using the snowball technique until theoretical saturation. Data were collected through semi-structured interviews. Data analysis was performed using the 2020 MAXQDA software. The final model was confirmed with 210 indicators, 24 categories, and 6 dimensions in the form of a paradigm model, with the green business entity as the central condition, causal conditions (business team), contextual conditions (green technology and green innovation), intervening conditions (risks), strategies (environmental factors), and financial services (consequences) for startup valuation. According to the findings, the category of environmental factors has the greatest impact on startup valuation, and investors should pay more attention to government support, international conditions, market characteristics, and processes when evaluating the green value of a startup. It was also observed that green technology and green innovation have a lower impact compared to other dimensions, as startups are formed based on a new idea and the use of technology.

Today, the world is on the verge of a new industrial revolution called Industry 5.0. This new paradigm is based on a combination of advanced technologies, such as artificial intelligence, the Internet of Things, robotics, virtual and augmented reality, and biotechnologies. With the rapid evolution of the Industry 4.0 paradigm and the emergence of the Industry 5.0 vision, there is an urgent need to examine and analyze the factors that facilitate or hinder effective technology transfer processes. Technology transfer refers to the deliberate transfer of knowledge from individuals or organizations that have created it to others so that it can be used to produce new products or deliver services. The present research aims to explore the key dimensions and components affecting technology transfer in the context of Industry 5.0, with a focus on the technological entrepreneurship approach, which is important because technological entrepreneurship plays a pivotal role in driving innovation and transforming scientific achievements into commercialized products and services.

The study adopted Sandelowski and Barroso’s qualitative meta-synthesis approach. The process began with a systematic review of 457 initial articles, and ultimately, 28 articles relevant to the research objective were selected. The inclusion criterion was publication in the fields of technological entrepreneurship and Industry 5.0 between 2017 and 2024. The reliability of the findings was assessed by the kappa coefficient (0.72), which indicated acceptable reliability. To validate the proposed model and increase its acceptability, the method of review by experts was utilized. Additionally, the results obtained were validated and reviewed by experts to ensure their confirmability.

Based on the findings of the meta-synthesis, 55 components were identified within five main dimensions as factors influencing technology transfer within the framework of Industry 5.0 with a technological entrepreneurship approach. They include (1) technological innovation ecosystems, including technological infrastructure, skilled human capital, and free flow of knowledge, (2) agile and flexible business models aligned with rapid technological changes, (3) collaborative partnerships among various actors in the innovation ecosystem, (4) digitalization of processes and data-driven decision-making, and (5) appropriate legal, policy, and supportive frameworks.

Conclusion/ Implications:

 The results highlight the importance of employing entrepreneurial strategies and approaches in facilitating and shaping technology transfer processes in the Industry 5.0 vision. The results indicate that success in technology transfer in this domain requires holistic attention to infrastructural, commercial, legal, and interactive factors within an integrated innovation ecosystem. By identifying and clarifying the key factors that play a role in successful technology transfer initiatives in the Industry 5.0 domain, this research provides insights and practical guidance for policymakers, technological entrepreneurs, and other stakeholders involved in these processes. These findings can contribute to strengthening technology transfer processes and guiding innovative orientations in the upcoming era of Industry 5.0.

The construction industry is the one of the biggest sources of pollution which has caused significant harmful effects on the environment.Adopting green supply chain management can be a suitable strategy to control and reduce harmful environmental effects.The aim of the current research is to identify and present a model of effective barriers to the implementation of green supply chain management in the construction industry.In this research, a mixed approach and a purposeful sampling method have been used.To identify the components, a meta-synthesis approach was used and then a semi-structured interview with 14 experts who were managers and contractors active in the construction industry,has been used. In order to analyze the data, was used the hidden content analysis strategy by using MAXQDA software. Finally,the components based on the power of influence and dependence were identified using the MICMAC analysis and the research model was presented with the interpretive-structure method. Based on the findings, the barriers affecting to the implementation of green supply chain management were classified into 16 main factors and into 2 categories of internal and external barriers. Also, the results have shown a ten-level model lack of government support as the most influential variable in the tenth level and the variables: non-cooperation between stakeholders, lack of support from senior and middle managers, desire to maintain current construction methods, lack of green supply chain management measures in the vision and mission of the company are ranked as the most influential variables of this model are placed at the first level.

 Regarding the importance of innovation and adopting appropriate strategies in the competitive advantage of banks, this paper tries to investigate the moderating effect of dynamic knowledge management capabilities on the relationship between innovation strategy and tourism bank performance.The statistical population of this research is the branches and headquarters of Tourism Bank in Tehran. The statistical sample was determined by Morgan's table of 247, and due to the probability of questionnaires falling, 15% was added to the sample size, which was 284. To assess the validity of the questionnaires, expert judgment was used and for reliability, Cronbach's alpha and SPSS software were used.Structural equations and AMOS software were used for data analysis.This research is an applied and descriptive-survey type.The results show that the dynamic knowledge management capabilities have a significant impact on the relationship between innovation strategy and performance in tourism bank.Other results also indicate that service innovation, process innovation, and organizational innovation have a significant impact on the performance of tourism banks by considering the moderating role of dynamic knowledge management capabilities. 

During the last years, the emergence of new technologies and the creation of great changes in global markets have made it more necessary to pay attention to technology and project portfolio management.

With the aim of analyzing the factors and components of the portfolio management of health R&D projects.Research

The research is applied in terms of purpose, which was conducted with the qualitative method of Sandusky and Barroso's seven-stage Metasynthesis model. The statistical population is all studies conducted in Iranian articles from 1393 to 1400 and foreign articles from 1998 to 2022, previous researches, databases.

In this regard, 260 researches in the field of the subject were evaluated and finally 60 articles were selected purposefully and by analyzing the content and combining the relevant literature with open, central and selective coding methods, a total of 100 open codes, 19 central codes, 14 The key category was selected and analyzed during the systematic search and combination process, and its validity was confirmed through Kaufman's kappa test.

key categories identified include; Financial management, intellectual capital management, technology management, IT management, R&D management, strategic management, project portfolio management, healthcare services management, health information technology management, quality management, political factors, cultural factors, economic factors and The legal factors were categorized into three categories: main, contextual, and environmental factors, and finally, after going through the seven stages of the research, the portfolio management model of R&D projects in the field of health services based on information technology was formulated.

The present research was conducted with the aim of presenting a sustainable innovation development model in information technology startups based on grounded theory approach. The method used in the research is qualitative and based on grounded theory strategy. An in-depth interview tool was used to collect data.The target population was the managers and senior experts of startups who participated in the sustainable innovation process and decisions of information technology startups, using targeted sampling after 11 in-depth interviews, theoretical saturation was achieved. In the foundation's data approach, in this research, during three stages of open, central and selective coding, 24 general categories have been expressed in a paradigmatic format, including causal, contextual, intervening conditions, central categories, strategies and consequences.The results showed that the sustainable innovation development in information technology startups by causal conditions (risk tolerance, analysis of the business situation of the startup's sustainable innovative capabilities and technology absorption capacity), contextual (organizational structure of startups, market orientation and organizational readiness), intervening (infrastructures legal, managerial factors, behavioral patterns, environmental factors and economic uncertainty), core categories (open sustainable innovation, technological capabilities and optimization of innovation processes), strategies (development of digital technologies, strategic planning for startups, value creation of startups and co-creation) and consequences (competitive advantage, strategic intelligence, business model improvement, digital transformation and organizational effectiveness) are realized.

The spillover of defense technologies in commercial businesses has an effective role in the development of technology, production, employment and economic growth of the country. The main purpose of this study is to design a model to evaluate and forecasting of defence technologies spillover to civillian with ANFIS. The research statistical population includes experts in the field of defence and civillian. The research is applied research in terms of method and according to the use of fuzzy inference system for modeling, it is of mathematical modeling type. The difference between the output values of ANFIS and the input data to an acceptable level of 6.6252*10-7 (the average error calculated in the model training stage) indicates the approximate compliance of the real data with the predicted data and implies It is designed on the lack of over-fitting phenomenon in ANFIS. In other words, the obtained model has a high power of prediction and we see a positive effect on the modeling ability to generalize. policies and laws with a weight criterion of 28.82% have the highest impact on the research model and it is necessary to consider the necessary policies and laws to make optimal use of the technological capabilities of the defense sector in the civilian sector. Also, communication factors, technological capability, environmental factors, socio-cultural factors, economic-financial factors and technological characteristics affect the model, respectively.

The expansion of conventional commercial banking from 1960s and 1970s gradually gained significant pace due to the development of digital industry, the increase of innovations in the banking sector and an accumulative utilization of the capabilities of scientists in the field of information technology. Over the past three decades, Iran's banking system has recognized Fintechs and has gradually turned from confronting and competing with Fintechs to cooperation and collaboration with them. In this article, in order to achieve a solid frame work of how the interactions and cooperation of Iran's banking system and Fintech companies are related to the determination and development of a comprehensive model, contributions to detailed studies has finally concluded to the necessity of using a strategic and flexible cooperation approach, in the structuring of collaboration between the banking system and Fintech companies. In this research, which is of a qualitative type, in order to provide a paradigmatic qualitative model based on grounded theory approach, 14 of best local experts were interviewed and Atlas T software, was used to present the results. The results of this research can not only be used by experts, activists and policy makers in this field to evaluate the consequences and trace optimal strategic interactions in the country's banking system, but also clearly show the necessity of entering into strategic collaboration between banking system and Fintech companies.

In this article, the results of the investigation of the key factors affecting the portfolio management of research and development projects based on information technology in the field of health and treatment are presented using the Delphi method. For this purpose, an explanation will be given first about the management of research and development project portfolio, and then the research problem will be defined by providing evidence from all the studies conducted in Iran and the world. The review of previous researches and studies in the field of key factors on the management of the portfolio of research and development projects on information technology in the field of health and treatment and their summary forms the next part of the article. In the following, the research method, especially the Delphi method, is explained, and at the end, the findings of this research are described and analyzed. To carry out this research, firstly, with library, studies and literature review of the research topic and the opinion of experts in the field of study, key factors and indicators were extracted on "management of the portfolio of research and development projects based on information technology in the field of health and treatment" and 67 indicators for research were identified. In this research, a 7-point Likert scale was used to obtain experts' opinions. Based on the findings of this research, the data of experts in this field were analyzed. These factors were categorized into three categories: main, contextual, and environmental factors, and at the end of the third round of Delphi, less important indicators were removed. At the end, limitations and suggestions are presented.

The purpose of this research is to identify the dimensions and components of the maturity model of the open innovation ecosystem in Iran's defense industries based on the purposeful use of knowledge flows, in the form of ideas, science or technology in order to create value that can bring significant benefits to the individuals and organizations, including accelerating the main processes inside the organization (internal innovation) or expanding the market (external innovation) outside the organization. This research is an applied-basic one. In order to do so, 485 papers in the field of open innovation were collected using the meta-synthesis method. Based on the algorithm for selecting the final articles, the data of 80 articles were analyzed, and the maturity model of open innovation ecosystem was designed using the coding method and Maxqda Analytics Pro Software. To check the quality or reliability of the indicators, Kappa coefficient was used in SPSS software, which is equal to 0.86 in the current research, that indicates the appropriate reliability of the indicators. The analysis of the concluding model and the findings of this research showed that the open innovation ecosystem has several components in forms of 13 categories and 23 concepts, including collaborations and joint ventures, investment in ideas and research, and crowdsourcing. Also, the results showed that participating in investment, making mutual contracts with companies active in the field of business, and acquiring small and knowledge-based companies can accelerate the main processes of innovation within the organization.