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This study aims to examine how the theory of planned behaviour and technology acceptance theory can be used to understand the adoption of non-pharmaceutical interventions (NPIs). The relationships between attitudes, behavioural intentions towards using NPIs, actual use of NPIs and word-of-mouth (WOM) were examined and compared between early and late adopters.

A survey was conducted to test the hypotheses with partial least squares structural equation modelling (n = 278).

The results indicate that relationships between attitudes, intentions and behavioural intentions were positive and significant in the whole data set – and that there were differences between the early and late adopters. WOM had no substantial relationship with actual usage and early adopters’ behavioural intentions.

This research gives a better sense of how WOM impacts attitudes, behavioural intentions and actual usage among early and late adopters of NPIs and highlights the effectiveness of WOM, especially among late adopters of NPIs. Furthermore, using the TAM allows us to make specific recommendations regarding encouraging the use of NPIs. A new three-stage communications model is introduced that uses early adopters as influencers to reduce the NPI adoption time by late adopters.

This survey explores the application of real options theory to the field of health economics. The integration of options theory offers a valuable framework to address these challenges, providing insights into healthcare investments, policy analysis and patient care pathways.

This research employs the real options theory, a financial concept, to delve into health economics challenges. Through a systematic approach, three distinct models rooted in this theory are crafted and analyzed. Firstly, the study examines the value of investing in emerging health technology, factoring in future advantages, associated costs and unpredictability. The second model is patient-centric, evaluating the choice between immediate treatment switch and waiting for more clarity, while also weighing the associated risks. Lastly, the research assesses pandemic-related government policies, emphasizing the importance of delaying decisions in the face of uncertainties, thereby promoting data-driven policymaking.

Three different real options models are presented in this study to illustrate their applicability and value in aiding decision-makers. (1) The first evaluates investments in new technology, analyzing future benefits, discount rates and benefit volatility to determine investment value. (2) In the second model, a patient has the option of switching treatments now or waiting for more information before optimally switching treatments. However, waiting has its risks, such as disease progression. By modeling the potential benefits and risks of both options, and factoring in the time value, this model aids doctors and patients in making informed decisions based on a quantified assessment of potential outcomes. (3) The third model concerns pandemic policy: governments can end or prolong lockdowns. While awaiting more data on the virus might lead to economic and societal strain, the model emphasizes the economic value of deferring decisions under uncertainty.

This research provides a quantified perspective on various decisions in healthcare, from investments in new technology to treatment choices for patients to government decisions regarding pandemics. By applying real options theory, stakeholders can make more evidence-driven decisions.

Decisions about patient care pathways and pandemic policies have direct societal implications. For instance, choices regarding the prolongation or ending of lockdowns can lead to economic and societal strain.

The originality of this study lies in its application of real options theory, a concept from finance, to the realm of health economics, offering novel insights and analytical tools for decision-makers in the healthcare sector.

The purpose of this paper is to investigate the impact of the COVID-19 pandemic on company innovation, specifically centering on the quantity and quality of innovation. The paper aims to provide a comprehensive understanding of whether the epidemic inhibits innovation and the role of digital transformation in mitigating this negative impact.

The paper uses a quasi-experimental study of the COVID-19 pandemic and constructs a differential model to analyze the relationship between the epidemic and firm innovation in three dimensions: total, quantity and quality. The paper also uses a difference-in-difference-in-differences model to test whether digital transformation of firms mitigates the negative impact of the epidemic and its mechanism of action.

The results show that COVID-19 significantly reduced the overall level of firm innovation, primarily in terms of quantity rather than quality. Furthermore, this study finds that digital transformation plays a pivotal role in mitigating the pandemic’s adverse impact on innovation. By addressing financing constraints and countering demand insufficiency, digital transformation acts as a catalyst for preserving and fostering innovation during and after the pandemic.

This study extends the current research on the pandemic’s impact on firm innovation at the micro level. It offers valuable insights into strategies for fostering digital transformation among Chinese enterprises in the post-pandemic era.