The inter-organizational learning approach has shown considerable growth in recent years. The concepts of trust and cooperation between individuals present in the New Economic Sociology (NES) approach of social relati...The inter-organizational learning approach has shown considerable growth in recent years. The concepts of trust and cooperation between individuals present in the New Economic Sociology (NES) approach of social relations emerge with great emphasis and are gaining prominence as a facilitator and propellant mechanism in transferring knowledge inside networks. This study aims to analyze the learning networks formed by fish-farmers organized into a cooperative to identify the characteristics of the network through structural analysis and identification of its critical actors. The study was performed with thirteen fish-farmers of a cooperative headquartered inMundo Novocounty, which is located inMato Grosso do Sulstate in the Midwest region of Brazil. Questionnaires and interviews with closed and open questions were used to collect the data. Measures of centrality (for individuals), density and reciprocity (in relations between individuals) were obtained from ORA software. The findings revealed that experience exchanges between fish-farmers, events participation, meetings attendance, speech audience and technical advice are the main forms of inter-organizational learning. Results suggest that the network has low levels of cohesion and that it is not taking advantage of its full potential. It was also found that there is a subgroup within the network that presents a high level of cohesion and shared learning based on cooperative informal relationships. Grounded in findings, suggestions for future research are presented.展开更多
Smart farming(SF)involves the incorporation of information and communication technologies into machinery,equipment,and sensors for use in agricultural production systems.New technologies such as the internet of things...Smart farming(SF)involves the incorporation of information and communication technologies into machinery,equipment,and sensors for use in agricultural production systems.New technologies such as the internet of things and cloud computing are expected to advance this development,introducing more robots and artificial intelligence into farming.Therefore,the aims of this paper are twofold:(i)to characterize the scientific knowledge about SF that is available in the worldwide scientific literature based on the main factors of development by country and over time and(ii)to describe current SF prospects in Brazil from the perspective of experts in this field.The research involved conducting semi-structured interviews with market and researcher experts in Brazil and using a bibliometric survey by means of data mining software.Integration between the different available systems on the market was identified as one of the main limiting factors to SF evolution.Another limiting factor is the education,ability,and skills of farmers to understand and handle SF tools.These limitations revealed a market opportunity for enterprises to explore and help solve these problems,and science can contribute to this process.China,the United States,South Korea,Germany,and Japan contribute the largest number of scientific studies to the field.Countries that invest more in R&D generate the most publications;this could indicate which countries will be leaders in smart farming.The use of both research methods in a complementary manner allowed to understand how science frame the SF and the mains barriers to adopt it in Brazil.展开更多
文摘The inter-organizational learning approach has shown considerable growth in recent years. The concepts of trust and cooperation between individuals present in the New Economic Sociology (NES) approach of social relations emerge with great emphasis and are gaining prominence as a facilitator and propellant mechanism in transferring knowledge inside networks. This study aims to analyze the learning networks formed by fish-farmers organized into a cooperative to identify the characteristics of the network through structural analysis and identification of its critical actors. The study was performed with thirteen fish-farmers of a cooperative headquartered inMundo Novocounty, which is located inMato Grosso do Sulstate in the Midwest region of Brazil. Questionnaires and interviews with closed and open questions were used to collect the data. Measures of centrality (for individuals), density and reciprocity (in relations between individuals) were obtained from ORA software. The findings revealed that experience exchanges between fish-farmers, events participation, meetings attendance, speech audience and technical advice are the main forms of inter-organizational learning. Results suggest that the network has low levels of cohesion and that it is not taking advantage of its full potential. It was also found that there is a subgroup within the network that presents a high level of cohesion and shared learning based on cooperative informal relationships. Grounded in findings, suggestions for future research are presented.
文摘Smart farming(SF)involves the incorporation of information and communication technologies into machinery,equipment,and sensors for use in agricultural production systems.New technologies such as the internet of things and cloud computing are expected to advance this development,introducing more robots and artificial intelligence into farming.Therefore,the aims of this paper are twofold:(i)to characterize the scientific knowledge about SF that is available in the worldwide scientific literature based on the main factors of development by country and over time and(ii)to describe current SF prospects in Brazil from the perspective of experts in this field.The research involved conducting semi-structured interviews with market and researcher experts in Brazil and using a bibliometric survey by means of data mining software.Integration between the different available systems on the market was identified as one of the main limiting factors to SF evolution.Another limiting factor is the education,ability,and skills of farmers to understand and handle SF tools.These limitations revealed a market opportunity for enterprises to explore and help solve these problems,and science can contribute to this process.China,the United States,South Korea,Germany,and Japan contribute the largest number of scientific studies to the field.Countries that invest more in R&D generate the most publications;this could indicate which countries will be leaders in smart farming.The use of both research methods in a complementary manner allowed to understand how science frame the SF and the mains barriers to adopt it in Brazil.