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The medical devices, biotechnology, and healthcare industries are closely tied to innovation, with companies relying on research and development and new product introduction. However, innovation activities like research and development can be costly, and commercializing new products faces complexities. Having effective business models (BMs) aligned with such innovation activities is crucial. This study delves into the intricacies of BMs in these innovator companies, emphasizing the pivotal role of effective BMs in navigating uncertainties and fostering innovation. This study conducted a systematic literature review to synthesize knowledge on BMs in innovator health-tech companies and compare models on dimensions like infrastructure, offering, customers, and finances. The review of 34 recent papers revealed 9 key BMs - open innovation, sustainable, dynamic, dual, spin-off, frugal, high-tech entrepreneurial content marketing, back-end, and product-service systems BMs. The analysis found open innovation, sustainability, and dynamicity as foundational models that can serve as a basis when combined with others. The paper unveils a tailored Dynamic Sustainable Business Model (DSBM) for Health-Tech, designed to integrate adaptability and sustainability, providing a framework for companies to leverage emerging technologies effectively. Additionally, a conceptual framework outlining 28 groups of uncertainty factors in BMs was developed to aid risk management in health-tech. The findings offer crucial insights for companies in health-tech industries, aiding them in managing innovation and value creation amidst a rapidly evolving landscape.

Today’s challenges to sustainability are explored through a complex combination of interdisciplinary topics that explore various interactions between economic, social, and environmental systems that further contribute to existing uncertainties. Solving complex/dynamic sustainability constraints does not demand exclusively technical and practical methods, as it is equally important to have a profound conceptual understanding of the origins of such challenges. The purpose of this study was to investigate the challenges to the sustainable development process from the perspective of the philosophy of grey systems theory (GST). GST considers inherent defects and shortcomings in human understanding/knowledge and identifies the roots of uncertainty. The study concentrates on the sustainable development process, highlighting the ways in which GST explains the causes and sources of uncertainty in this process. It is emphasized that sustainability cannot be achieved without intentional human intervention, and that international collaboration is vital in solving sustainability problems. Uncertainty and challenges to sustainable development stem from human grey understanding and knowledge. This problem makes it difficult for humans to understand and model dynamicity, to strike a balance between different spheres of science, and to have an objective view of reality due to the dependence of knowledge on thinking paradigms and values. These shortcomings ultimately bring about value conflicts, different understandings of risks, and impediments to international collaboration and agreement. Finally, the study explains that uncertainty arises from incomplete understanding and grey knowledge, and that uncertainty undermines the prediction of outcomes. Furthermore, delays inherent in interactions and the impacts of diverse systems on the world increase uncertainty and complicate decision- and policymaking in improvement projects. In their efforts to implement their decisions and policies, humans also encounter various limitations in terms of their capacities, resources, and facilities. The application of GST-based approaches to the operational area is also discussed.

Purpose – The purpose of this paper is to propose a framework for data analytics where everything is grey in nature and the associated uncertainty is considered as an essential part in data collection, profiling, imputation, analysis and decision making. Design/methodology/approach – A comparative study is conducted between the available uncertainty models and the feasibility of grey systems is highlighted. Furthermore, a general framework for the integration of grey systems and grey sets into data analytics is proposed. Findings – Grey systems and grey sets are useful not only for small data, but also big data as well. It is complementary to other models and can play a significant role in data analytics. Research limitations/implications – The proposed framework brings a radical change in data analytics. It may bring a fundamental change in our way to deal with uncertainties. Practical implications – The proposed model has the potential to avoid the mistake from a misleading data imputation. Social implications – The proposed model takes the philosophy of grey systems in recognising the limitation of our knowledge which has significant implications in our way to deal with our social life and relations. Originality/value – This is the first time that the whole data analytics is considered from the point of view of grey systems.

Scientific prediction and accurate grasp of the future trend of population change are conducive to the formulation of different population policies at different stages, so as to alleviate the adverse effects of the aging population on society and provide scientific theoretical reference for controlling the population size and making policy. Considering that the population system is affected by many complex factors and the structural relationship among these factors is complex, it can be regarded as a typical dynamic grey system. In this paper, the fractional-order GM (1, 1) model and the fractional-order Verhulst model are established, respectively, based on the statistical data of China's population indices from 2015 to 2019 to forecast the population size and the change trend of population structure of China from 2015 to 2050 in the short-term and medium- to long-term. The forecast results show that China’s population will grow in an inverse S shape from 2015 to 2050, when the total population will reach 1.43 billion. Moreover, during this period, the birth rate and natural growth rate of population will decrease year by year, and the proportion of aging population and the dependency ratio of population will increase year by year. Besides, the problem of aging population is going to become increasingly serious. The application of grey system method to population prediction can mine the complex information contained in the population number series. Meanwhile, the fractional-order accumulation can weaken the randomness of the original data series and reduce the influence of external disturbance factors, so it is a simple and effective population prediction method.