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.

Rapid urban expansion gives rise to smart cities which pose immense logistical and supply chain challenges. The COVID-19 pandemic transformed the holistic system identified by Zhao et al. in 2021. The system encompasses logistics and supply chain integral to the concept of smart cities, with a focus on sustainability. This transformation requires an in-depth study on challenges of a common framework of policies for smart cities in countries comprising the Organisation for Economic Cooperation and Development (OECD). The study employs an extensive literature analysis for the period 2020–2022. an approach which contextualizes the model. The model identifies the causes, impact, and spillovers of new trends in logistics and supply, including the sustainability of adopted technologies. The study includes the variables involved, and barriers to creating a shared model. The results reveal that the two elements affecting the supply chain and transport in smart cities are Industry 4.0 and 5.0 technologies supporting specific sectors. The resilience of small and medium-sized enterprises positively impacts the sustainability of large urban centres. The study presents both factors that help and hinder the adoption of environmental, social, and economic sustainability technologies.

Urban regeneration and gentrification are complex, interconnected processes that significantly shape cities. However, these phenomena in the Middle East and North Africa (MENA) region are often understudied and typically viewed through a Western lens. This systematic review of literature from 2010 to 2024 addresses this gap by synthesizing a comprehensive framework for understanding urban regeneration-led gentrification in MENA countries. The review delves into key themes: Gentrification contexts, the regeneration process, gentrification accelerators, and the aftermath of gentrification. It explores the diverse motives behind urban regeneration, identifies key stakeholders, and analyzes catalysts of gentrification. Findings reveal that informal areas and deteriorated heritage sites in major cities are most susceptible to gentrification. The study also highlights the critical issue of insufficient community participation and proposes a participation evaluation framework. The unique socioeconomic and political factors driving gentrification in the MENA region underscore the necessity of context-specific approaches, facilitating the identification of regional similarities and differences. Conclusively, the review asserts that gentrification is a cyclic process, necessitating core interventions through enhanced regeneration strategies or displacement plans to mitigate its effects.

Adequate sanitation is crucial for human health and well-being, yet billions worldwide lack access to basic facilities. This comprehensive review examines the emerging field of intelligent sanitation systems, which leverage Internet of Things (IoT) and advanced Artificial Intelligence (AI) technologies to address global sanitation challenges. The existing intelligent sanitation systems and applications is still in their early stages, marked by inconsistencies and gaps. The paper consolidates fragmented research from both academic and industrial perspectives based on PRISMA protocol, exploring the historical development, current state, and future potential of intelligent sanitation solutions. The assessment of existing intelligent sanitation systems focuses on system detection, health monitoring, and AI enhancement. The paper examines how IoT-enabled data collection and AI-driven analytics can optimize sanitation facility performance, predict system failures, detect health risks, and inform decision-making for sanitation improvements. By synthesizing existing research, identifying knowledge gaps, and discussing opportunities and challenges, this review provides valuable insights for practitioners, academics, engineers, policymakers, and other stakeholders. It offers a foundation for understanding how advanced IoT and AI techniques can enhance the efficiency, sustainability, and safety of the sanitation industry.

Depression is a mental disorder caused by various causes with significant and persistent depressed mood as the main clinical feature, and is the most common mental illness worldwide and in our country. The number of patients with depression worldwide was as high as 350 million in 2017, and the number of patients with depression in our country was nearly 100 million in 2019. The greatest danger of depression is self-injurious and suicidal behaviour, and this behaviour carries a high medical burden. Medication is the most costly treatment for depression in China, and while it is an effective way to treat patients with depression, it has many side effects and poor patient compliance. Non-pharmacological treatments commonly used in clinical practice include physiotherapy and psychotherapy. Physiotherapy is commonly used in non-convulsive electroconvulsive therapy, but its clinical efficacy is uncertain and it can also cause adverse effects such as heart failure and arrhythmias, which are poorly tolerated by patients. Psychotherapy is also a common non-pharmacological therapy. Cognitive therapy is a common form of psychotherapy, but the cycle of cognitive therapy is too long, the cost to the patient is high, and the patient’s cognitive ability has certain requirements. Music therapy is a combination of art and science. It is a cross-discipline that combines body, movement, dance and psychology and is a method of psychotherapy that has biological, psychological and social functions to compensate for deficiencies. Music therapy sees a fundamental connection between mind and body and emphasises that what affects the body also affects the mind. When mind-body integration is lacking, individuals will suffer from a variety of psychological disorders. Therefore, the core principles of music therapy emphasise that holistic individual health is embodied in the integration of mind and body, that body movement is expressive and communicative, and that music therapy uses body movement as a method of assessing the individual and as a means of clinical intervention.

This systematic literature review examines data saturation in qualitative research within the context of entrepreneurship studies from 2004 to 2024. Data saturation, a critical concept in ensuring the rigor of qualitative research, remains inadequately defined in terms of sample size and assessment criteria across various studies. This review synthesizes 11 empirical studies, focusing on strategies such as stopping criterion, code frequency counts, and comparative methods for determining saturation. It identifies sample sizes ranging from 7 to 39 interviews, with an average saturation occurring between 10 and 12 interviews. Furthermore, the study explores the influence of different sampling methods and homogeneity of study populations on saturation outcomes. Despite the reliability of existing methods, the findings underscore the need for greater transparency and consistency in reporting saturation criteria. The review offers valuable insights for entrepreneurial researchers aiming to design qualitative studies, emphasizing the importance of tailored saturation standards based on research objectives and methodologies. This research contributes to a clearer understanding of data saturation in entrepreneurial studies and highlights the necessity for further empirical investigation into saturation across diverse qualitative methods.