Date of Award

2017

Degree Type

Thesis

Degree Name

Master of Science (Computer Science)

School

School of Science

First Advisor

Dr Leisa Armstrong

Second Advisor

Dr Gurpeet Kohli

Field of Research Code

080501, 080609, 070106

Abstract

The agricultural industry is integral to efforts of governments to feed an increasing world population. Its importance for the production of animals, plants, fibre, biofuel, has increased as climate change and other economic factors impact on food security.

Innovations in technologies and portable devices have made positive impacts in agriculture. Farm management software, precision agriculture, automatic power systems, GPS sensors, RFID and crop sensors are now widely used in agricultural production systems throughout the world. Portable devices are pervasive in all parts of society including the agricultural industry. Cloud computing has brought new opportunities in the agricultural industry to increase productivity by providing new approaches to process and store agricultural data acquired from the field to large datacentres. The adoption of this technology is dependent on agricultural industry stakeholders understanding of how this innovative technology could be best used in their agricultural and business practices.

The aim of this research is to investigate the factors determining the adoption of cloud computing (CC) in the agricultural industry in Australia. The research assessed the current understanding and usages of cloud computing in agricultural industry and examined the drivers and barriers in the adoption of the technology. A framework for the cloud computing adoption was also developed for an Australian agriculture context.

The research was carried out as a case study based approach using mixed methods methodology. It consists of a literature review, questionnaires, interviews and quantitative data collection. This study carried out a situational analysis for different agricultural companies to understand their current situation regarding their IT infrastructure. Questionnaires and interviews were conducted for data collection and analysis of the current situation. Both private and government agricultural companies were investigated for the study. A total of 250 Australian agricultural companies, farm associations, farm federations and small farms were invited to participate in this research. System integrators and cloud solution providers, ICT solutions providers as well as organisations which are involved in agriculture research were contacted to take part in the questionnaire and interview study. This research gathered and analysed data related to agencies infrastructure, service providers (both internal and external), computer systems, database, applications, existing or future cloud services. Various hypotheses were examined to understand the influence of cloud computing adoption factors in the Australian agricultural industry. The hypotheses were designed based on Technological, Organisational and Environmental (TOE) framework, Diffusion of Innovation (DOI) theory and Technology Acceptance Model (TAM) which assist in determining positive or negative influence of the factor to adopt or reject new technology, particularly cloud computing in agriculture.

Based on findings of this research a framework was developed for the cloud computing adoption in Australian agricultural industry for both private and government sectors. Questionnaire and interview analysis revealed four major elements which influence the adoption of cloud computing in Australian agriculture. These included Organisational, People, Technological and Environmental elements. Each element included a list of crucial factors of cloud computing adoption. Considerations and suggestions regarding adoption were developed in the proposed framework.

The research provides further insight into the cloud computing adoption in the Australian agricultural industry context and provides strategies to private and government agricultural industries which will assist agricultural stakeholders to determine the best approaches its integration into current agricultural and business processes.

Available for download on Tuesday, September 27, 2022

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