Intelligently driven performance management: an enabler of real-time research forecasting for innovative commercial agriculture

Abstract

Research in commercial agriculture is instrumental to achieve the targets set under the second Sustainable Development Goal (SDG), i.e., ‘Zero Hunger by 2030.’ Execution of research for the success of commercial agriculture becomes a tedious task and research organizations have been long struggling to assess their performance unequivocally in the face of COVID-19. Any evacuation plan in place to improve the performance, monitoring, and evaluation of a research institute must guarantee that the institute is on the right momentum and let it evades metrics-obsessed research drives during this pandemic. A survey was conducted through the participation of the topmost administrators attached to key research institutes working on agriculture in Sri Lanka to explore the current performance management practices deeply. The conclusions derived from a thematic analysis of the survey data were used to propose a set of solutions that facilitate a well-thought research agenda in a digitally transformed performance management system. The solutions imply that intelligently driven key performance measurements worked by artificial intelligence and big data could be used with policy innovations to support research integrity and assessment security within the coexistence of humans and machines for the well-being of research development in the commercial agriculture sector.Supplementary InformationThe online version contains supplementary material available at 10.1007/s43545-022-00484-8.