درخت حوزه‌های تخصصی

Today, business environments are fast changing and Nanotechnology-driven firms are opening up their organizational boundaries to tap into an external source for R&D activities. To reform the structure of the R&D activities in order to be successful, firms face challenges in finding a proper place in the R&D ecosystem. Therefore, this research examines the open R&D ecosystem through these key questions. Which components can form an open R&D ecosystem and how do interactions behave in a dynamic model? This research is done in 3 stages; the first stage is data collection based on the Fuzzy questionnaire, then the data analysis through the Fuzzy-DEMATEL method, and finally modeling the dynamics of the open R&D ecosystem.. This research is applied in terms of purpose and descriptive survey information in terms of collecting information. The findings suggest that Nano-tech in Iran relies on two main components that affect each other: technological innovation performance and ecosystems. the result shows that inside the ecosystem, the most share of activities is related to the human resources and governance sub-ecosystems. In addition, scientific works under the concept of technological innovation performance have the greatest impact from sub-ecosystems, and the balance between performance and ecosystem is a possibility for Nano-firms to do more R&D activities. Overall, this study provides a better understanding of open R&D by suggesting that further opening of the R&D ecosystem is necessary for nanotechnology.

Innovation capability refers to a complete set of characteristics of the organization that facilitates innovation strategies, and the food industry plays a pivotal role in the processing of agricultural raw materials and food supply. The aim of the present study was to Identify and Analyze of the dimensions of innovation capability, clustering and ranking indexes in Urmia food industries. The sample size of the statistical population encompassing 221 companies was equal to 143 according to Cochran's formula. Obtaining required information was based on field study method. In the applied part of the study, exploratory factor analysis, clustering with K-MEANS algorithm and cluster ranking based on AHP technique were used. The results of the exploratory factor analysis showed that the relationship of each item with the factors classified into three categories was properly expressed. Companies with similar features but different from other clusters were put into 8 clusters. Finally, according to the weights of study variables, "human", "operability" and "structural” factors ranked first, second and last respectively.

This study will seek to provide the best and most efficient ordering policies for different levels of the perishable food supply chain in order to maximize the overall profit of the chain, and minimize social and environmental damage. Our supply chain includes a four-level supply chain of suppliers, manufacturers, distributors and retailers. In addition to the components of perishable products, the dimensions of sustainable development have been fully investigated and taken into account in the entire chain. In this study, due to the main nature of perishable materials, the statistical population includes 9 dairy companies and 9 meat and protein companies, which in total 18 dairy, meat and protein companies in Fars province, Iran, were studied. Network Data Envelopment Analysis (DEA) was used to analyze the data and WinQsb was used to model and solve it. According to the results, the average efficiency of the supply chain of production and distribution of perishable products in the financial year studied by the research in the suppliers sector was equal to 0.9634. This average was equal to 0.9899 in the producers sector, 0.9903 in the distributors sector and 0.9707 in the retailers sector. Therefore, the average efficiency in the study shows that the most inefficiency problems of the studied companies are related to the supplier sector. Also, the overall average efficiency is equal to 0.9950.

Intelligence is an essential factor in supply chain. It prepares the ground for effective and timely communication and facilitates information sharing among supply chain components in both production and service sectors. Hence, the present study seeks to identify the components of services intelligent supply chain and categorize them as a comprehensive model by using the meta-synthesis approach. In this research, total of 437 articles in the field of services intelligent supply chain was identified and examined at first step. Articles were in the period of 1980 to 2022. Finally, after evaluation of articles, 53 articles remained. Then, based on the process of the meta-synthesis and the use of coding method, four main fields and a number of sub-fields were found for the services intelligent supply chain variable. Initial codes introduced in the software for conceptualization and categorization. The main fields of services intelligent supply chain included intelligence dimensions, intelligence conditions, intelligence strategies and intelligence consequences. Each of these main fields has a number of sub-fields. The result of the study shows that in order to implement the services intelligent supply chain, several dimensions and components are considered, concepts such as real-time online information flow, intelligent fund flow, intelligent services flow, process management and technology infrastructure play an important role. Suggestions are presented in accordance with the comprehensive model identified. Also, the conceptual model designed can be useful as a basis for future research in the direction of practical manual in the effective use of the services intelligent supply chain.