Business model innovation (BMI) has garnered substantial academic and corporate attention in recent decades. Researchers have not yet agreed on the most complicated BMI practices in the high-tech startups (HTS). Despite being the second-biggest economy in the world today, China has done little research on the practice of business model innovation in China’s high-tech startups. This study addresses the factors that impact the business model innovation of high-tech startups in China. Our study aims to fill the research gap by visualising and analysing, using systematic literature review (SLR) analyses and reviewing 36 in-depth articles, from 688 academic literature sources. Relevant publications from Scopus, Springer, ScienceDirect, Web of Science, IEEE Xplore, and the JDM e-library expose the current research status from 2013 to December 2023 without bias. We conducted a literature-based investigation to identify essential insights on the BMI factors in the literature and derived a high-tech startup’s BMI critical factor. Our study shows that three main factors affect the innovation of business models in high-tech startups in China. The findings raise managers’, entrepreneurs’, and executives’ knowledge of corporate resource bricolage and cognitive style constraints in business model innovation and their pros and cons. The findings will help Chinese academics understand enterprises’ institutional environment and resource bricolage as final suggestions and proposals for corporates, regulators, and policymakers are presented.

The major goal of decisions made by a business organization is to enhance business performance. These days, owners, managers and other stakeholders are seeking for opportunities of modelling and automating decisions by analysing the most recent data with the help of artificial intelligence (AI). This study outlines a simple theoretical model framework using internal and external information on current and potential clients and performing calculations followed by immediate updating of contracting probabilities after each sales attempt. This can help increase sales efficiency, revenues, and profits in an easily programmable way and serve as a basis for focusing on the most promising deals customising personal offers of best-selling products for each potential client. The search for new customers is supported by the continuous and systematic collection and analysis of external and internal statistical data, organising them into a unified database, and using a decision support model based on it. As an illustration, the paper presents a fictitious model setup and simulations for an insurance company considering different regions, age groups and genders of clients when analysing probabilities of contracting, average sales and profits per contract. The elements of the model, however, can be generalised or adjusted to any sector. Results show that dynamic targeting strategies based on model calculations and most current information outperform static or non-targeted actions. The process from data to decision-making to improve business performance and the decision itself can be easily algorithmised. The feedback of the results into the model carries the potential for automated self-learning and self-correction. The proposed framework can serve as a basis for a self-sustaining artificial business intelligence system.

The Circular Economy is one of the most prominent cross-disciplinary and cross-sectoral concepts to emerge in recent decades. It has permeated academia, policymaking, business, NGOs, and the general public, leading to numerous applications of the concept, some of which only partially overlap. In this article, we review recent debates and research trends in the Circular Economy, outlining the ten most common groups of its conceptualizations using the PRISMA (Preferred Items for Systematic Reviews and Meta-Analysis) method. We then propose a post disciplinary and transnational research program on the Circular Economy that would not only combine hard and soft sciences in unprecedented ways but also have important practical applications, such as developing tools to embed the Circular Economy in natural, technical, economic, and socio-cultural settings.

Analysing external factors with a design-thinking approach is crucial for adaptation, identifying opportunities, and mitigating risks in native digital enterprises. This research introduces a framework rooted in design principles and future scenarios for external analysis, with the aim of meeting current market needs. The study employs a mixed qualitative-quantitative research approach, incorporating methods such as literature review, workshops, and surveys. These methods enable the collection and analysis of both qualitative and quantitative data, providing a comprehensive and accurate understanding of the research topic by using it in a DNVB case study. Developing a conceptual framework using a design-thinking approach which we call ASPECT contributes to a comprehensive interpretation of complexity, intertwining collective and individual factors. This reduces the risk of overlooking essential elements when making strategic decisions in ambiguous, uncertain, and volatile contexts. This method contrasts with traditional external analysis frameworks like CAME, Pestle, and SWOT. The document aims to contribute to the literature by exploring new models of external analysis based on the design process. This framework combines the conventional stages of a design thinking process with methodologies for future scenarios to identify relevant external factors for organizations. It provides an innovative conceptual framework for creating new business models and growth strategies for digital enterprises.

In Industry 4.0, the business model innovation plays a crucial role in enabling organizations to stay competitive and capitalize on the opportunities presented by digital transformation. Industry 4.0 is driven by digitalization and characterized by integrating various emerging technologies. These technologies can potentially change traditional business models and create new value propositions for customers. This paper aims to analyze and review the research papers through a bibliometric approach scientifically. The data were extracted from reputable Clarivate Web of Science (WoS) Core Collection sources from 2010 to 2023 (June). However, the publication started in 2018 for the research fields. The results show that scientific publications on research domains have increased significantly from 2020. VOSviewer, R Language, and Microsoft Excel were utilized for analysis. Bibliometric and Scientometric approaches conducted to determine and explore the publication patterns with significant keywords, topical trends, and content clustering better discussions of the publication period. The visualization of the data set related to research trends of Industry 4.0 in relation to Business Model Innovation resulted in several co-occurrence clusters namely: 1) Business Model Innovation; 2) Industry 4.0; 3) Digital transformation; and 4) Technology implementation and analysis. The study results would identify worldwide research trends related to the research domains and recommendations for future research areas.