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The 21st century has brought the business model earth-shaking changes, especially since the Corona Virus Disease 2019 (COVID-19) epidemic at the end of 2019. Now, the epidemic normalization is slowing down China's rapid development. However, technological development, like artificial intelligence (AI), is unstoppable and is transforming China's economic growth modes from factor-driven to innovation-driven systems. Therefore, it is necessary to study further the new changes in labor entrepreneurship and innovation business models and their mechanism of action on economic growth.

This work studies how innovative human capital (IHC) uses AI and other scientific and technological (S&T) innovation technologies to promote China's innovation-driven economic growth model transformation from the labor entrepreneurship and innovation perspective.

The research shows that the entrepreneurial innovation ability of IHC can increase marginal return and output multiplier effect. It changes the traditional business model and promotes China's economic growth and innovation development. At the same time, this work analyzes China's inter-provincial panel data through the panel smooth transition regression (PSTR) model. It concludes that there is a nonlinear relationship between IHC and the output of innovative achievements. The main body presents three stages of nonlinear changes: first rising, then slightly declining, and rising so far.

The finding provides a direction for solving the problem of slow economic growth and accelerating the transformation of economic growth mode under epidemic normalization.

The purpose of this paper is to asses the welfare and macroeconomic implications of three distinct degrowth strategies designed to reduce carbon emissions: penalizing fossil fuel demand, substituting aggregate consumption with leisure and disincentivizing total factor productivity (TFP) growth.

Using an environmental dynamic general equilibrium (eDGE) model that incorporates both green renewable technologies and fossil fuels in the production process, this study sets an emissions reduction target aligned with the goals of the Paris Agreement by 2050.

The results reveal that the conventional degrowth strategy, wherein a reduction in the consumption of goods and services is compensated with an increase in leisure, may entail significant economic consequences, leading to a notable decline in welfare. In particular, a degrowth scenario resulting from a decline in TFP yields the most pronounced reduction in welfare. Conversely, inducing a reduction in fossil fuel demand by fiscally inflating the price of the imported commodity, despite potential social backlash, exhibits noticeably less detrimental welfare effects compared to other degrowth policies. Furthermore, under this degrowth strategy, the findings suggest that a globally coordinated strategy could result in long-term welfare gain.

To the best of the authors’ knowledge, this is the first contribution that uses an eDGE model to evaluate the welfare implications of an additional degrowth strategy amidst the ongoing inertial reduction of carbon emissions.

This study aims to comprehensively analyze the intersection of technology management and innovation management amidst the fourth industrial revolution, uncovering evolving trends and influential contributors.

Using the Bibliometrix R-package, this pioneering research conducts a bibliometric analysis to delve into innovation and technology management literature, quantifying scholarly output and identifying thematic breakthroughs.

The study reveals quantitative insights into the progression of innovation and technology management research, offering guidance on evolving trends, thematic breakthroughs and influential contributors.

The findings offer valuable insights for practitioners and managers, guiding them through emerging trends and recommending a dual focus on fundamental principles and emerging areas for strategic decision-making.

By fostering active engagement with evolving trends, this research contributes to the ongoing technology and innovation management discourse, potentially leading to societal benefits and advancements.

This study pioneers an in-depth bibliometric analysis at the intersection of innovation and technology management, offering unique insights and quantitative assessments of scholarly output and thematic trends, thus adding significant value to the existing literature.

The main objective of the study is to address the lack of sustainability assessments of smart connected health systems in the academic literature by presenting an assessment model to determine the alignment of these systems with the 17 Sustainable Development Goals (SDGs) proposed in the 2030 Agenda.

An evaluation model based on decision analysis is proposed that includes three phases: alignment framework, information gathering and assessment. This model measures the alignment of the connected health system with each of the 17 SDGs, identifying the goals and criteria associated with each SDG that the system achieves to satisfy.

The analysis reveals that the system has achieved more than 24% of the targets among the 17 SDGs. In addition, it identifies four sustainability challenges that the system potentially addresses in relation to the SDGs, providing valuable guidance for researchers and practitioners interested in sustainable health technology development.

The study's results have significant implications for policymakers and stakeholders in the health and technology sectors.

The originality of this study lies in its comprehensive approach to assessing the sustainability of connected health systems in the context of the SDGs, filling an important gap in the existing literature.

The primary aim of this study is to identify and analyze the key digital transformation areas and determine their impact on technological unemployment in the textile sector. In addition, this study explores whether digital transformation contributes to neo-Luddism or Robot Breaking.

The advent of digital transformation has raised significant concerns, particularly concerning technological unemployment. This study focuses on conducting an analytical hierarchical process (AHP) analysis to determine the impact of different disciplines within digital transformation on technological unemployment. The investigation specifically delves into the ongoing transition to Industry 4.0 within the textile industry. This study uses a mixed-method approach, consisting of a literature review, bibliometric analysis, eight expert phenomenological interviews, and AHP.

This study revealed that artificial intelligence, machine learning and deep learning are the most crucial disciplines that will affect the concept of neo-Luddism. The fact that technological unemployment in the textile sector is examined with AHP Analysis makes this study unique.

This study contributes to Industry 4.0 literature by examining the nexus of technological unemployment in textile manufacturing and the emergence of neo-Luddism.

This study investigates the relationships among digital transformation, technological innovation, industry–university–research collaborations and labor income share in manufacturing firms.

The relationships are tested using an empirical method, constructing regression models, by collecting 1,240 manufacturing firms and 9,029 items listed on the A-share market in China from 2013 to 2020.

The results indicate that digital transformation has a positive effect on manufacturing companies’ labor income share. Technological innovation can mediate the effect of digital transformation on labor income share. Industry–university–research cooperation can positively moderate the promotion effect of digital transformation on labor income share but cannot moderate the mediating effect of technological innovation. Heterogeneity analysis also found that firms without service-based transformation and nonstate-owned firms are better able to increase their labor income share through digital transformation.

This study provides a new path to increase the labor income share of enterprises to achieve common prosperity, which is important for manufacturing enterprises to better transform and upgrade to achieve high-quality development.

As Industry 4.0 revolutionizes workplaces with unprecedented technological advancements, this chapter underscores the paramount importance of prioritizing human well-being and engagement. It navigates through a comprehensive array of strategies and practices that empower organizations to forge a work environment that is not only technologically advanced but also profoundly supportive, gratifying and motivating for employees (Froschauer et al., 2021). By elucidating how organizations can empower employees with autonomy while fostering collaborative endeavours, it uncovers a pathway to empowerment and job satisfaction (Caldarola et al., 2019; Kadir & Broberg, 2021). This chapter illustrates how organizations can harness these technologies to provide tailored growth experiences, thereby contributing to a thriving workforce. Navigating the ethical landscape of the digital workplace, this chapter examines the profound implications of Industry 4.0 on employee well-being. Delving into issues of privacy, transparency and equitable treatment, it imparts essential considerations for organizations seeking to align their practices with ethical imperatives. The methodology will incorporate case studies specific to the UAE market, providing a localized lens through which to analyze and implement human-centred workplace strategies (Mütze-Niewöhner et al., 2022; Urrutia Pereira et al., 2022). This chapter presents a holistic guide for organizations seeking to infuse human-centred principles into their Industry 4.0 workplaces (Caldarola et al., 2019; Longo et al., 2022). By championing well-being, job satisfaction and fulfilment, it equips leaders and decision-makers with actionable strategies to cultivate a work culture that thrives amid the rapid march of technological progress (Aromaa et al., 2019; Froschauer et al., 2021).

This paper aims to investigate the role of virtual training in supporting Sustainable Development Goals (SDGs) and reducing workforce inequalities. It analyzes both the advantages and challenges of integrating virtual training within sustainable development frameworks and the circular economy, underlining its crucial role in transforming workforce strategies and enhancing economic and social progress.

The study uses Becker’s human capital theory (1964) and Bandura’s social learning theory (1977) to explore the impact of virtual training on human capital development and its contributions to specific SDGs. This integrated approach provides a detailed exploration of how virtual training intersects with sustainable development initiatives.

The findings highlight the pivotal role of technology in training and development, particularly in the post-pandemic landscape. Virtual training significantly enhances global collaboration, inclusivity and sustainability. It highlights the necessity of adapting corporate training practices to digital environments, thereby improving the quality of education, advancing gender equality and stimulating economic growth.

This paper presents unique perspectives on the influence of workplace training in the post-pandemic era, focusing on technological integration. It discusses how such integration supports diversity, equity and inclusion within the workforce and highlights the essential role of virtual training in promoting organizational flexibility and enhancing employee skills amidst ongoing digital transformations.

The aim of this study is to discern the role of digital finance in driving rural industrial integration and revitalization. Specifically, it intends to shed light on how the deep development of digital finance can contribute to the optimization and transformation of the rural industrial structure. The research further explores the particular effects of this financial transformation in the central and western regions of China.

This research studies the influence of digital finance on rural industrial integration across 30 Chinese provinces from 2011 to 2020. Utilizing the entropy weight method, a comprehensive evaluation index system is established to gauge the level of rural industrial integration. A two-way fixed effects model, intermediary effect model, and threshold effect model are employed to decipher the relationship between digital finance and rural industrial integration.

Findings reveal a positive relationship between digital finance and rural industrial integration. A single threshold feature was identified: beyond a traditional finance development level, the marginal effect of digital finance on rural industrial integration increases. These effects are more noticeable in central and western regions.

Empirical outcomes contribute to policy discourse on rural digital finance, assisting policymakers in crafting effective strategies. Understanding the threshold of traditional finance development provides a new perspective on the potential of digital finance to drive rural industrial integration.