The Effects of Individual Variables, Farming System Characteristics and Perceived Barriers on Actual Use of Smart Farming Technologies: Evidence from the Piedmont Region, Northwestern Italy
暂无分享,去创建一个
[1] Tom R. Wielicki,et al. A knowledge-driven shift in perception of ICT implementation barriers: Comparative study of US and European SMEs , 2010, J. Inf. Sci..
[2] Anne Collins McLaughlin,et al. Aging farmers are at high risk for injuries and fatalities: how human-factors research and application can help. , 2011, North Carolina medical journal.
[3] Mario Coccia,et al. Likely Technological Trajectories in Agricultural Tractors by Analysing Innovative Attitudes of Farmers , 2015 .
[4] M. van Persie,et al. Spatio-temporal Analysis of Remote Sensing and Field Measurements for Smart Farming , 2015 .
[5] R. Grout,et al. Non-adoption of environmental innovations in wine growing , 2013 .
[6] I. Sturgess. The Future of the Common Agricultural Policy , 1990 .
[7] K. V. D. Klauw. ALLIANCE FOR INTERNET OF THINGS INNOVATION , 2015 .
[8] M. Bavorova,et al. Adoption of Agri-Environmental Measures by Organic Farmers: The Role of Interpersonal Communication , 2015 .
[9] Gregory M. P. O'Hare,et al. Modelling the smart farm , 2017 .
[10] Eugenio Cavallo,et al. Machinery-Related Perceived Risks and Safety Attitudes in Senior Swedish Farmers , 2017, Journal of agromedicine.
[11] S. Wolfert,et al. Big Data in Smart Farming – A review , 2017 .
[12] Peter Gluchowski,et al. Data Warehouse , 1997, Informatik-Spektrum.
[13] Yeong Sheng Tey,et al. Factors influencing the adoption of precision agricultural technologies: a review for policy implications , 2012, Precision Agriculture.
[14] E. Giannakis,et al. Off-Farm Employment and Economic Crisis: Evidence from Cyprus , 2018 .
[15] P. Bentler,et al. Comparative fit indexes in structural models. , 1990, Psychological bulletin.
[16] Giacomo Carli,et al. 6 th International Conference on Information and Communication Technologies in Agriculture, Food and Environment (HAICTA 2013) Drivers of Precision Agriculture Technologies Adoption: A Literature Review , 2013 .
[17] J. Manyika,et al. Disruptive technologies: Advances that will transform life, business, and the global economy , 2013 .
[18] Keith F Widaman,et al. Confirmatory factor analysis and item response theory: two approaches for exploring measurement invariance. , 1993, Psychological bulletin.
[19] Peter Lundqvist,et al. Health and safety strategy in Swedish agriculture. , 2012, Work.
[20] Stan G. Daberkow,et al. Farm and Operator Characteristics Affecting the Awareness and Adoption of Precision Agriculture Technologies in the US , 2003, Precision Agriculture.
[21] Andreas Ritter,et al. Structural Equations With Latent Variables , 2016 .
[22] V. Corte,et al. Scientific development of smart farming technologies and their application in Brazil , 2017 .
[23] M. Coccia,et al. Attitudes and Behaviour of Adopters of Technological Innovations in Agricultural Tractors: A Case Study in Italian Agricultural System , 2014 .
[24] X. Phạm,et al. How data analytics is transforming agriculture , 2018 .
[25] A. Adrian,et al. Producers' perceptions and attitudes toward precision agriculture technologies , 2005 .
[26] Margarita Genius,et al. Information Transmission in Irrigation Technology Adoption and Diffusion: Social Learning, Extension Services, and Spatial Effects , 2014 .
[27] Andrea Knierim,et al. Experience versus expectation: farmers’ perceptions of smart farming technologies for cropping systems across Europe , 2019, Precision Agriculture.
[28] R. Gasson,et al. THE FARM AS A FAMILY BUSINESS: A REVIEW , 1988 .
[29] G. Brunori,et al. Knowledge networks and their role in shaping the relations within the Agricultural Knowledge and Innovation System in the agroenergy sector. The case of biogas in Tuscany (Italy) , 2017 .
[30] Dorit Kerret,et al. Food for Hope: The Role of Personal Resources in Farmers’ Adoption of Green Technology , 2018 .
[31] Mário Otávio Batalha,et al. Factors influencing the adoption of Farm Management Information Systems (FMIS) by Brazilian citrus farmers , 2017, Comput. Electron. Agric..
[32] Achim Walter,et al. Opinion: Smart farming is key to developing sustainable agriculture , 2017, Proceedings of the National Academy of Sciences.
[33] J. H. Steiger. Structural Model Evaluation and Modification: An Interval Estimation Approach. , 1990, Multivariate behavioral research.
[34] Adriana Bruggeman,et al. Water pricing and irrigation across Europe: opportunities and constraints for adopting irrigation scheduling decision support systems , 2016 .
[35] F. Magnotti,et al. The innovation capacity of small food firms in Italy , 2017 .
[36] S. Wheeler. What influences agricultural professionals' views towards organic agriculture? , 2008 .
[37] S. Presser,et al. Questions and Answers in Attitude Surveys: Experiments on Question Form, Wording, and Context , 1996 .
[38] M. Reichardt,et al. Adoption and future perspective of precision farming in Germany: results of several surveys among different agricultural target groups , 2009, Precision Agriculture.
[39] Stan G. Daberkow,et al. Socioeconomic Profiles of Early Adopters of Precision Agriculture Technologies , 1998 .
[40] E. Cavallo,et al. An ergonomic approach to sustainable development: The role of information environment and social‐psychological variables in the adoption of agri‐environmental innovations , 2019, Sustainable Development.
[41] A. Hayes. Beyond Baron and Kenny: Statistical Mediation Analysis in the New Millennium , 2009 .
[42] Thomas B. Long,et al. Barriers to the adoption and diffusion of technological innovations for climate-smart agriculture in Europe: evidence from the Netherlands, France, Switzerland and Italy , 2016 .
[43] OECD Science, Technology and Innovation Outlook 2020 , 2018, OECD Science, Technology and Innovation Outlook.
[44] Claire Massey,et al. The technology acceptance model and use of technology in New Zealand dairy farming , 2004 .
[45] Eugenio Cavallo,et al. Falls From Agricultural Machinery: Risk Factors Related to Work Experience, Worked Hours, and Operators’ Behavior , 2018, Hum. Factors.
[46] E. Cavallo,et al. It does not occur by chance: a mediation model of the influence of workers’ characteristics, work environment factors, and near misses on agricultural machinery-related accidents , 2017, International journal of occupational and environmental health.
[47] Mauro Villarini,et al. Smart Machines, Remote Sensing, Precision Farming, Processes, Mechatronic, Materials and Policies for Safety and Health Aspects , 2018 .
[48] Knowledge integration and the adoption of new agricultural technologies: Kenyan perspectives , 2012, Food Security.
[49] Joanne Sneddon,et al. Modelling the faddish, fashionable and efficient diffusion of agricultural technologies: A case study of the diffusion of wool testing technology in Australia , 2011 .