Major Determinants of Innovation Performance in the Context of Healthcare Sector

Abstract

Through the innovation network (IN) and the use of artificial intelligence (AI), this study aims to look into the innovation performance (IP) of the healthcare industry. Digital innovation (DI) is also tested as a mediator. For the collection of data, cross-sectional methods and quantitative research designs were used. To test the study hypotheses, the SEM technique and multiple regression technique were used. Results reveal that AI and the innovation network support the attainment of innovation performance. The finding demonstrates that the relationship between INs and IP links and AI adoption and IP links is mediated through DI. The healthcare industry plays a vital role in facilitating public health and improving the living standards of the people. This sector’s growth and development are largely dependent on its innovativeness. This study highlights the major determinants of IP in the healthcare industry in terms of IN and AI adoption. This study adds to the literature’s knowledge via an innovative proposal in which the mediation role of DI among IN-IP and AI adoption-innovation links is investigated.

What is the impact of healthcare innovation on measurable outcomes of healthcare organisation performance? A systematic review

“…Joshi et al 54 acknowledge a range of barriers that may impact the implementation of AI in healthcare including low awareness, low investment, and low commitment from top-level management. Future research should focus on addressing such barriers, given the intricate links between AI development and innovation performance 55…”

“…Those best placed to cultivate such a climate are healthcare leaders; management support has been considered a key facilitator in innovation implementation and diffusion12 52 72 and identified as the predominant contributor of an organisational climate for innovation 73. Recent research55 suggests that hospital owners and senior management ought to focus on the development and integration of innovation networks, which will encourage organisations to support digital innovation practices through AI activities and support innovation performance of the healthcare industry. Nassani et al 55 further suggest that government and enterprises at an organisational level can encourage innovative practices by offering incentives in addition to economic support, including subsidies, depreciation allowances and tax exemptions.…”

“…Recent research55 suggests that hospital owners and senior management ought to focus on the development and integration of innovation networks, which will encourage organisations to support digital innovation practices through AI activities and support innovation performance of the healthcare industry. Nassani et al 55 further suggest that government and enterprises at an organisational level can encourage innovative practices by offering incentives in addition to economic support, including subsidies, depreciation allowances and tax exemptions.…”

A Physics-Informed Recurrent Neural Network for RRAM Modeling

“…In the Verilog-A model, the hidden states are represented by node voltages of the controlled current source, and n equals 20. Each current source is connected to the ground and implements Equation (12), which is the difference of the LHS and RHS of Equation (11). The current flowing through each current source equals zero.…”

“…An effective strategy to combat these modeling challenges is to adopt neural network (NN) techniques since for emerging devices, high-quality measurement data are often more readily available than comprehensive physical understanding. In several application domains, machine learning has already demonstrated some promise [10][11][12][13][14]. Neural networks using back propagation can model the relationships between different variables with certain generalization capabilities.…”