Redirigiendo al acceso original de articulo en 16 segundos...
Inicio  /  Agriculture  /  Vol: 12 Par: 8 (2022)  /  Artículo
ARTÍCULO
TITULO

Technology Acceptance, Adoption and Workforce on Australian Cotton Farms

Nicole McDonald    
Eloise S. Fogarty    
Amy Cosby and Peter McIlveen    

Resumen

The future of work is influenced by the digital transformation of industries, including agriculture. The current study aimed to understand the social drivers of automated technology acceptance and adoption in Australian cotton farms. The study employed a mixed-methods approach to compare those who were (a) currently using automated technology, (b) not currently using automated technology but considering adoption, and (c) not currently using automated technology and no intention to adopt. The research found that social factors and workforce considerations influence growers? motivation to adopt automated technology on farms. Furthermore, differences on appraisals of perceived usefulness were observed when comparing growers with no intention to adopt automated technology with those considering adoption or who have adopted automated technology. Both perceived usefulness and ease of use barriers are challenges for those considering adoption of automated technology. Support that improves ease of use for those who have adopted automated technology is important for continued appraisals of perceived usefulness of automated technology. Further research to understand antecedents to appraisals of perceived usefulness and ease of use, and how these interact to influence acceptance and automated technology, is required to inform strategic workforce interventions that support the digital transformation of cotton farms.

 Artículos similares

       
 
Hao-Ran Qu and Wen-Hao Su    
Weeds and crops engage in a relentless battle for the same resources, leading to potential reductions in crop yields and increased agricultural costs. Traditional methods of weed control, such as heavy herbicide use, come with the drawback of promoting w... ver más
Revista: Agronomy

 
Luana Centorame, Thomas Gasperini, Alessio Ilari, Andrea Del Gatto and Ester Foppa Pedretti    
Machine learning is a widespread technology that plays a crucial role in digitalisation and aims to explore rules and patterns in large datasets to autonomously solve non-linear problems, taking advantage of multiple source data. Due to its versatility, ... ver más
Revista: Agronomy

 
Yaqin Ren, Hui Feng and Tianzhi Gao    
Soil degradation and declining soil fertility are prominent issues for sustainable agricultural development in China. Therefore, it is of great significance to promote the adoption rate of conservation agriculture technology. Risk cognition and technolog... ver más
Revista: Agriculture

 
Diana Martínez-Arteaga, Nolver Atanacio Arias Arias, Aquiles E. Darghan and Dursun Barrios    
Water is one of the most determining factors in obtaining high yields in oil palm crops. However, water scarcity is becoming a challenge for agricultural sustainability. Therefore, when the environmental supply of water is low, it is necessary to provide... ver más
Revista: Agriculture

 
Shah Johir Rayhan, Md. Sadique Rahman and Kaiyu Lyu    
Rice agriculture provides millions of households with a steady source of income and employment. However, for small and marginal farmers, the exorbitant cost of production inputs presents a formidable obstacle in their pursuit of acquiring it. Credit cons... ver más
Revista: Agriculture