Digital technologies, innovation, and skills: Emerging trajectories and challenges

Smart city and green innovation: Mechanisms and industrial emission reduction effect

Green innovation is essential for environmentally sustainable development. The construction of smart cities offers significant potential for developing green innovation through optimizing urban administration and improving the allocation of critical resources. Using Chinese city data from 2005 to 2019, this study adopts a causal identification framework based on the multi-temporal difference-in-difference method to explore the impact of smart city construction on green innovation and the mechanism and joint industrial emission reduction effect between them. A positive and significant relationship with a weak inverted U-shaped trend was found between smart city construction and green innovation. Besides the direct channel, labor factor allocation, venture capital attractiveness, and market accessibility are essential indirect channels between the two concepts. Furthermore, the effects of smart city construction on green innovation varied depending on the marketization level, administrative rank, population size, and geographic location of the city. In addition, the interaction of the two constructs negatively affected industrial emissions, which helped optimize the environment. These findings suggest that smart city construction offers a digital dividend for developing green innovation and creating an efficient, sustainable environment.

The impact of technological innovation on the green digital economy and development strategies

To investigate the interplay among technological innovation, industrial structure, production methodologies, economic growth, and environmental consequences within the paradigm of a green economy and to put forth strategies for sustainable development, this study scrutinizes the limitations inherent in conventional deep learning networks. Firstly, this study analyzes the limitations and optimization strategies of multi-layer perceptron (MLP) networks under the background of the green economy. Secondly, the MLP network model is optimized, and the dynamic analysis of the impact of technological innovation on the digital economy is discussed. Finally, the effectiveness of the optimization model is verified by experiments. Moreover, a sustainable development strategy based on dynamic analysis is also proposed. The experimental results reveal that, in comparison to traditional Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB) models, the optimized model in this study demonstrates improved performance across various metrics. With a sample size of 500, the optimized model achieves a prediction accuracy of 97.2% for forecasting future trends, representing an average increase of 14.6%. Precision reaches 95.4%, reflecting an average enhancement of 19.2%, while sensitivity attains 84.1%, with an average improvement of 11.8%. The mean absolute error is only 1.16, exhibiting a 1.4 reduction compared to traditional models and confirming the effectiveness of the optimized model in prediction. In the examination of changes in industrial structure using 2020 data to forecast the output value of traditional and green industries in 2030, it is observed that the output value of traditional industries is anticipated to decrease, with an average decline of 11.4 billion yuan. Conversely, propelled by the development of the digital economy, the output value of green industries is expected to increase, with an average growth of 23.4 billion yuan. This shift in industrial structure aligns with the principles and trends of the green economy, further promoting sustainable development. In the study of innovative production methods, the green industry has achieved an increase in output and significantly enhanced production efficiency, showing an average growth of 2.135 million tons compared to the average in 2020. Consequently, this study highlights the dynamic impact of technological innovation on the digital economy and its crucial role within the context of a green economy. It holds certain reference significance for research on the dynamic effects of the digital economy under technological innovation.

The impact of digital economy on rural revitalization: Evidence from Guangdong, China

Background

Digital technology and economy have profound impact on rural revitalization and become important initiatives to promote rural development. This study analyzes the spatial effect of digital economy on rural revitalization in Guangdong, China.

Method

The entropy weight method is used to evaluate the level of rural revitalization and digital economy in 20 prefecture level cities of Guangdong province during 2011-2021. Spatial panel model is used to analyze the spatial spillover effect of digital economy on rural revitalization. Threshold model is employed to analyze the marginal effects of the digital economy on rural revitalization. Heterogeneous analysis is made by grouping the prefecture-level cities into four regions: the Pearl River Delta (PRD), the East Wing, the West Wing and the Mountainous region.

Results

Both the level of rural revitalization and digital economy development in every prefecture-level city experienced an increase during 2011-2021 and the PRD region take the lead in both the rural revitalization and digital economy. There is significant spatial spillover effect of digital economy on the rural revitalization as a whole (the coefficient is about 0.3537). However, in the regions with a low level of digital economic development, there is a negative impact (-0.1847) of the digital economy on rural revitalization; while in regions with medium or high level of digital economy, it has a significant positive effect (0.3835) on rural revitalization. The impact of digital economy on rural revitalization development is significantly greater in the PRD (0.3141), the East Wing (0.7215), the West Wing (0.5467) than in the mountainous region (0.2238).

Conclusions

There is significant spatial differentiation in the digital economy and rural revitalization. The digital economy has significant spatial spillover effect on the rural revitalization. And this effect is found out to have heterogeneous and non-linear characteristics. In order to promote rural revitalization, it is necessary to make full use of the digital economy and technology in rural areas. Besides, it is important to improve the digital skills and literary of the rural population so that the digital economy could play a greater role in rural revitalization.

Impacts of workplace automation on energy poverty: The new challenge of achieving SDG 7 in the context of technological revolution

This paper systematically examines how workplace automation impacts energy poverty from a demand-side perspective, revealing a new challenge for Sustainable Development Goal 7 (SDG 7) in the context of technological revolution. Our research demonstrates that workplace automation significantly increases household energy poverty. This finding is robust when using the instrumental variable approach to tackle endogeneity, as well as employing different automation and energy poverty measures, placebo tests, and machine learning methods for robustness checks. Automation's impact mechanism is that it reduces people's income and work-related social capital, thus exposing households to higher risks of energy poverty. Moreover, its consequences are more prominent for rural households, less educated people, non-migrants, those without labor contracts, non trade-union members, and out-of-system workers. Thus enhancing human capital, promoting free movement of workers, and providing better labor protection contribute to weakening the adverse impact of the technological shock. Meanwhile, we find that improving the price reasonability, stability, security and accessibility of energy supply can also mitigate the negative effects of workplace automation on household energy consumption. In the dual context of the fourth technological revolution promoting industrial automation as well as the increasing urgency to achieve SDG 7, findings of this paper have important policy implications.

Does Artificial Intelligence Promote or Inhibit On-the-Job Learning? Human Reactions to AI at Work

This paper examines how AI at work impacts on-the-job learning, shedding light on workers’ reactions to the groundbreaking AI technology. Based on theoretical analysis, six hypotheses are proposed regarding three aspects of AI’s influence on on-the-job learning. Empirical results demonstrate that AI significantly inhibits people’s on-the-job learning and this conclusion holds true in a series of robustness and endogeneity checks. The impact mechanism is that AI makes workers more pessimistic about the future, leading to burnout and less motivation for on-the-job learning. In addition, AI’s replacement, mismatch, and deskilling effects decrease people’s income while extending working hours, reducing their available financial resources and disposable time for further learning. Moreover, it has been found that AI’s impact on on-the-job learning is more prominent for older, female and less-educated employees, as well as those without labor contracts and with less job autonomy and work experience. In regions with more intense human–AI competition, more labor-management conflicts, and poorer labor protection, the inhibitory effect of AI on further learning is more pronounced. In the context of the fourth technological revolution driving forward the intelligent transformation, findings of this paper have important implications for enterprises to better understand employee behaviors and to promote them to acquire new skills to achieve better human–AI teaming.

Unfolding sustainable auditing ecosystem formation path through digitalization transformation: How digital intelligence of accountant fosters the digitalization capabilities

This research aims at indulging a consistent image of how Digitalization Capabilities (DC) demonstrates an impact on Sustainable Auditing Ecosystem (SAE) within public sector organizations (PSOs) in developing countries, especial in an emerging market, and gives insights regarding to divergent impacts of the interconnection between DC and SAE moderated by different levels of Accountants' Digital Intelligence (ADI). The state-of-the-art structural equation modelling (SEM) was employed to statistically analyze the survey data collected from self-administered questionnaire distributed to a convenient sample of 323 respondents. Additionally, multi-group analysis (MGA) was implemented to explore the heterogeneity of data and examined substance of the difference in the parameters caused by the moderating variable. The obtained results advocated that it was paramount for PSOs to increase DC for SAE formulation. The MGA findings highlighted a statistically significant difference in the impacts of DC on SAE between various levels of ADI. The insights acquired from final model emerged as a foundation which might buttress policymakers and PSOs' practitioners in initiatives development pertaining to digitalization for SAE formulation.

COVID-19 and entrepreneurship entry and exit: Opportunity amidst adversity

We theoretically and empirically examine how acquiring new skills and increased financial worries influenced entrepreneurship entry and exit intentions during the pandemic. To that end, we analyze primary individual-level survey data we collected in the aftermath of the COVID-19's first wave in Russia, which has had one of the highest COVID-19 infection rates globally. Our results show that acquiring new skills during the pandemic helped owners keep their existing businesses and encouraged start-ups in sectors other than information technology (IT). For IT start-ups, having previous experience matters more than new skills. While the pandemic-driven financial worries are associated with business closure intentions, they also inspire new business start-ups, highlighting the pandemic's creative destruction power. Furthermore, preferences for formal employment and remote work also matter for entrepreneurial intentions. Our findings enhance the understanding of entrepreneurship formation and closure in a time of adversity and suggest that implementing entrepreneurship training and upskilling policies during recurring waves of the COVID-19 pandemic can be an important policy tool for innovative small business development.

Linking Digital Capacity to Innovation Performance: the Mediating Role of Absorptive Capacity

Digital technologies are considered as factors that accelerate the pace of innovation and increase the firm’s innovation performance. However, few studies have investigated whether this claim is conditioned by other elements that contribute to innovation. Furthermore, firms increasingly rely on external knowledge sources to expand their internal knowledge base for the development of innovations. In this context, absorptive capacity can be considered as an essential organizational capability to embrace adoption of digital technologies and enhance their positive effect on innovation performance. This paper builds on this discussion and studies the contribution of digital capacity on innovation performance, proposing the mediating role of absorptive capacity in the context of the digital transformation. It uses evidence from an extensive Greek survey in 1014 manufacturing firms and analyzes the complex relationships underlying the role of digital transformation to innovation. The contribution of the paper is two-fold: (i) it provides a deeper insight into the underlying mechanisms through which firms can leverage their digital capacity to accelerate innovation, and (ii) it highlights the important mediating role of absorptive capacity in enhancing the positive effects of digitalization indicating that digital capacity is not an unquestionable asset for innovation performance. Accordingly, our results show a positive direct contribution of digital capacity to innovation performance, which is enhanced in the presence of absorptive capacity as a mediator. In fact, the indirect effect of digital capacity to innovation performance through absorptive capacity is stronger. These findings present important policy implications, as there is need for improvement in other innovation-related aspects of the business ecosystem to efficiently address the challenge of digital transformation, such as R&D efforts, training, interaction among actors, and building of communities of practice.

Towards Sustainable Development: How Digitalization, Technological Innovation, and Green Economic Development Interact with Each Other

Green technological innovation is one of the endogenous drivers of green economic growth, and digitalization can promote green economic development in the form of industrial empowerment. The interactive relationship and the degree of influence between digitalization, technological innovation, and green economic development is thus an urgent issue to be addressed. Based on the panel data of 30 Chinese provinces from 2011 to 2019, we measured digitalization, technological innovation, and green economic development for the first time using the entropy method and included them in the same analytical framework by constructing a PVAR model to empirically test their interrelationship and degree of influence. Our findings suggest that: (1) There is an inertial development and self-reinforcing mechanism among the three variables. (2) The impact of digitalization on green economic development has a positive promotion effect, while the impact of technological innovation on green economic development is not significant. (3) The impact of green economic development on technological innovation has a positive promotion effect in the short term, but this effect gradually declines and tends to zero in the long term. Finally, based on the findings, several practical suggestions are made.

Digital Transformation and Corporate Social Performance: How Do Board Independence and Institutional Ownership Matter?

This study addresses a gap in the literature on corporate governance and corporate social responsibility (CSR) by investigating whether and how board independence and institutional ownership moderate the relationship between digital transformation and corporate social performance (CSP). We find that digital transformation increases CSP using a panel dataset of Chinese publicly listed firms between 2014 and 2018. Moreover, we show that this positive impact is more pronounced when firms have higher proportions of independent directors on the board and institutional owners. These findings contribute to a better understanding of CSR dynamics, supporting the formulation and implementation of efficient CSR strategies in the digital era.

Using role play to develop an empathetic mindset in executive education

Purpose

The purpose of this paper is to discuss the importance of a role play activity as part of an experiential instructional strategy to develop an empathetic mindset among professionals, managers and executives attending an executive education program in change management.

Design/methodology/approach

This paper provides an approach and process for management educators and facilitators of executive education programs to introduce and teach role play for the busy executives to learn about empathy.

Findings

Role play is a useful teaching method that helps adult learners understand the importance of seeing things from another person’s point of view especially within a short period of time.

Practical implications

Management educators and facilitators could introduce and teach role play for the busy executives to learn about empathy based on the proposed approach and process in this paper.

Originality/value

This paper provides an approach and process for management educators and facilitators of executive education programs to introduce and teach role play for the busy executives to learn about empathy especially within a short a period of time.