Drone as a Service (DaaS) in promoting Cleaner Agricultural Production and Circular Economy for Ethical Sustainable Supply Chain Development

In order to grow the food the world needs, there is a pressing need to gain a more detailed understanding of how innovative solutions can be incorporated into the agricultural supply chains, particularly within production, for environmentally, economically, ethically and socially viable food production. Despite a number of innovative solutions available, many challenges in agricultural supply are still prevalent, with researchers to date largely focusing on these challenges in isolation, as opposed to exploring the relationships held between these challenges. Thus, supported by Circular Economy, Agriculture, Industry 4.0 literature and expert opinions, agricultural supply chain challenges are modelled and analysed using ISM methodology to help uncover 12 agricultural challenges which ultimately impede goods moving within the supply chain. Findings discovered that the Unproductive Workers and Pesticide Hazards are the key drivers of agricultural challenges. The ISM Hierarchical model elucidates research propositions and a parsimonious model for future research.

Influencing subjective well-being for business and sustainable development using big data and predictive regression analysis.

Business leaders and policymakers within service economies are placing greater emphasis on well-being, given the role of workers in such settings. Whilst people's well-being can lead to economic growth, it can also have the opposite effect if overlooked. Therefore, enhancing subjective well-being (SWB) is pertinent for all organisations for the sustainable development of an economy. While health conditions were previously deemed the most reliable predictors, the availability of data on people's personal lifestyles now offers a new dimension into well-being for organisations. Using open data available from the national Annual Population Survey in the UK, which measures SWB, this research uncovered that among several independent variables to predict varying levels of people's perceived well-being, long-term health conditions, one's marital status, and age played a key role in SWB. The proposed model provides the key indicators of measuring SWB for organisations using big data.