Beyond scientific contribution: Assessment of the societal impact of research and innovation to build a sustainable agri-food sector

What is the societal impact of university research? A policy-oriented review to map approaches, identify monitoring methods and success factors

The discussion on the 'societal impact of university research' has emerged within the recent decade with different utilisation from the British REF impact stories to the EU Framework Programmes' criteria. The fundamental idea is that (university) research should contribute to addressing societal challenges, such as climate change, and not just pursue 'scientific excellence'. Several approaches have been developed, from case studies of the 'productive interactions' that co-create knowledge with societal stakeholders to the so-called 'altmetrics' tracking online dissemination of research outputs. Nonetheless, these experiences do not always point in the same direction, and the notion of 'societal impact' seems still unclearly defined. Given the growing policy pressure on universities to address societal challenges, this paper reviews the scientific literature on the so-called 'societal impact' of university research. By querying three scientific archives (Web of Science, Scopus and ScienceDirect), 135 relevant scientific publications are selected and discussed, looking for conceptualisations, monitoring methods and success factors. The review maps a value-laden discussion arguing for longer-term, multi-dimensional perspectives on university research impacts beyond bibliometric indicators. Findings highlight a progressive shift from 'attribution', i.e., looking for causal relationships between research and societal changes, to 'contribution', acknowledging researchers' efforts to engage with societal challenges.

Innovation of Platform Economy Business Model Driven by BP Neural Network and Artificial Intelligence Technology

In order to enhance the competitiveness of enterprises, how to evaluate and enhance the competitiveness of B2B e-commerce enterprises and promote the orderly and healthy development of B2B e-commerce industry are discussed. This paper puts forward the research on the innovation of platform economic business model driven by BP neural network and artificial intelligence technology. BP neural network is used to study and evaluate the competitiveness of B2B e-commerce companies. According to the B2B e-commerce company competitiveness theory and BP neural network algorithm, combined with BP neural network and B2B e-commerce company competitiveness evaluation index system, a BP neural network model is designed to analyze the competitiveness of B2B e-commerce enterprises. Determine the expected value of network samples, select G1 method to determine the subjective weight, and select entropy weight method to determine the objective weight. With the help of the function in the MATLAB neural network toolbox, the neural network is trained. The results show that when the training times reach 3297 times, the sample mean square error is 9.9869e − 06, and the training network reaches convergence. The samples of three enterprises test the trained neural network and input the data of three test samples into the trained BP neural network, and the output results are 0.1531, 0.1371, and 0.1557, respectively. The network model constructed in this paper is effectively close to the training samples. The established BP neural network has good performance and can be used to evaluate the competitiveness of B2B e-commerce companies. Accelerate technological change and realize innovation. Technological capability is the inexhaustible driving force for the development of enterprises. Only with the innovation of keeping pace with the times can application-oriented e-commerce enterprises meet the needs of customers and the market, form the difference between goods or services, and then enable enterprises to win more customers and market share.