Environmental air pollution management system: Predicting user adoption behavior of big data analytics

The impact of digital inclusive finance on the collaborative reduction of pollutant and carbon emissions: spatial spillover and mechanism analysis

In light of the escalating global climate risks threatening human survival, there is a global consensus on the necessity for collaborative reduction of pollutant and carbon emissions (CRPC). Within this context, digital inclusive finance (DIF) is recognized for its unique inclusiveness and digital characteristics as a critical factor in promoting environmentally friendly and sustainable development. DIF provides advantageous channels for environmental governance, thereby making the achievement of CRPC objectives feasible. However, the impact of DIF on CRPC has not been fully explored. This study employs a spatial econometric model to investigate the impact of DIF on CRPC in 278 prefecture-level cities in China from 2011 to 2020. The findings indicate that DIF has a positive impact on CRPC, with significant spatial spillover effects. The analysis highlights the pivotal mediating roles played by technology effect and electrified effect of the energy mix, while environmental regulation effect plays a moderating role. Notably, disparities in the impact of DIF on CRPC are evident, particularly in non-resource-based cities, cities with low carbon intensity, and eastern regions where spatial spillover effects are more pronounced. These experiences enrich the relevant thesis in terms of DIF on CRPC, providing a theoretical basis for formulating CRPC schemes.

Breathing in danger: Understanding the multifaceted impact of air pollution on health impacts

Air pollution, a pervasive environmental threat that spans urban and rural landscapes alike, poses significant risks to human health, exacerbating respiratory conditions, triggering cardiovascular problems, and contributing to a myriad of other health complications across diverse populations worldwide. This article delves into the multifarious impacts of air pollution, utilizing cutting-edge research methodologies and big data analytics to offer a comprehensive overview. It highlights the emergence of new pollutants, their sources, and characteristics, thereby broadening our understanding of contemporary air quality challenges. The detrimental health effects of air pollution are examined thoroughly, emphasizing both short-term and long-term impacts. Particularly vulnerable populations are identified, underscoring the need for targeted health risk assessments and interventions. The article presents an in-depth analysis of the global disease burden attributable to air pollution, offering a comparative perspective that illuminates the varying impacts across different regions. Furthermore, it addresses the economic ramifications of air pollution, quantifying health and economic losses, and discusses the implications for public policy and health care systems. Innovative air pollution intervention measures are explored, including case studies demonstrating their effectiveness. The paper also brings to light recent discoveries and insights in the field, setting the stage for future research directions. It calls for international cooperation in tackling air pollution and underscores the crucial role of public awareness and education in mitigating its impacts. This comprehensive exploration serves not only as a scientific discourse but also as a clarion call for action against the invisible but insidious threat of air pollution, making it a vital read for researchers, policymakers, and the general public.

From theory to practice: An integrated TTF-UTAUT study on electric vehicle adoption behavior

In Industry 4.0, the adoption of new technology has played a major role in the transportation sector, especially in the electric vehicles (EVs) domain. Nevertheless, consumer attitudes towards EVs have been difficult to gauge but researchers have tried to solve this puzzle. The prior literature indicates that individual attitudes and technology factors are vital to understanding users' adoption of EVs. Thus, the main aim is to meticulously investigate the unexplored realm of EV adoption within nations traditionally reliant on oil, exemplified by Saudia Arabia. By integrating the "task technology fit" (TTF) model and the "unified theory of acceptance and usage of technology" (UTAUT), this research develops and empirically validates the framework. A cross-section survey approach is adopted to collect 273 valid questionnaires from customers through convincing sampling. The empirical findings confirm that the integration of TTF and UTAUT positively promotes users' adoption of EVs. Surprisingly, the direct effect of TTF on behavioral intentions is insignificant, but UTAUT constructs play a significant role in establishing a significant relationship. Moreover, the UTAUT social influence factor has no impact on the EVs adoption. This groundbreaking research offers a comprehensive and holistic methodology for unravelling the complexities of EV adoption, achieved through the harmonious integration of two well-regarded theoretical frameworks. The nascent of this research lies in the skilful blending of technological and behavioral factors in the transportation sector.

Does digital economy development affect urban environment quality: Evidence from 285 cities in China

The impact of the digital economy (DE) on urban environmental quality (EQ) is a critical aspect of China's economic development. This study investigates the impact of DI on urban EQ using the data from prefecture-level cities spanning the period from 2011 to 2021 and updates some disparate conclusions of related studies. It is discovered that a non-linear correlation exists between DE and urban EQ. Currently, DE can effectively improve local city EQ. This conclusion remains valid even after robustness tests and endogeneity treatment. The impact of DE on improving EQ can be classified as the impact of technological innovation, industrial upgrading, resource allocation, infrastructure construction, environmental governance, and changes in public lifestyle. Heterogeneity analysis reveals that the influence of DE is particularly pronounced in cities located in central and eastern regions of China, those with higher levels of administrative management, resource-based urban areas, and those with more stringent environmental regulations.

Digital financial development, synergistic reduction of pollution, and carbon emissions: evidence from biased technical change

Environmental pollution control and greenhouse gas emissions reduction have become the main ecological protection issues. The digital transformation of the financial sector provides a vital opportunity to holistically promote environmental governance. This article incorporates the synergistic reduction of pollution and carbon emissions into the environmental governance system of digital financial development. Using panel data from 280 cities in China between 2011 and 2018, we examine the impact of digital financial development on the synergistic reduction of pollution and carbon emissions. We find that (1) digital finance development can significantly improve the synergistic reduction of pollution and carbon emissions, effectively lowering carbon emissions while reducing pollution. External environmental concerns can further unleash the potential for digital finance development to enhance its synergistic reduction of pollution and carbon emissions. (2) The key transmission mechanism lies in the dual guidance of digital finance development toward biased technical change, i.e., toward energy-saving elements on the input side and toward reduced pollution output on the output side, thereby inducing the synergistic reduction of pollution and carbon emissions. (3) The synergistic effect of digital finance development on pollution and carbon reduction depends on the necessary regional development endowment, such as strong green technology innovation capabilities, lower traditional financial accessibility, and carbon sink reserves. This study expands the understanding of the environmental effects of digital finance development and offers crucial insights for exploring the optimal development path under green strategies.

Digital economy and environmental governance performance: empirical evidence from 275 cities in China

Along with the steady worsening of environmental contamination, improving environmental governance performance (EGP) is an urgent issue that needs to be addressed. This study adopts panel data from 275 Chinese cities to examine the holistic relationship between the digital economy (DE) and urban EGP in multiple dimensions. The relationship is tested using fixed-effects models, mediated effects models, panel threshold models, and the spatial Durbin models. Results indicated that (1) the DE can effectively improve urban EGP. The findings still hold after robustness tests using instrumental variable estimation and the introduction of exogenous policy shocks. (2) The analysis of mediating effects shows that green technology innovation and industrial structure optimization are critical transmissions. (3) The panel threshold regression results indicate a nonlinear incremental phenomenon of the significant impact of the DE on urban EGP when nighttime lighting data is used as the threshold. (4) Spatial spillover analysis shows a positive spatial spillover effect of DE development on EGP in the surrounding cities. (5) Spatial heterogeneity analysis states that the enhancement effect of DE development on EGP is more significant in eastern China. Furthermore, areas located within urban agglomerations are more influenced by the DE. These findings provide a policy reference for fully exploiting the environmental enhancement effects of the DE.

The influencing mechanism of big data analytics technology capability on enterprise's operational performance: The mediating role of data-tool fit

With the development of network technology, enterprises face the explosive growth of data every day. Therefore, to fully mine the value of massive data, big data analysis (BDA) technology has become the key to developing the core competitiveness of enterprises. However, few empirical studies have investigated the influencing mechanism of the BDA capability of an enterprise on its operational performance. To fill this gap, this study explores how BDA technology capability influences enterprise operation performance, based on dynamic capabilities theory and resource-based theory. It proposes the key variables, including the connectivity, compatibility, and modularization of big data analysis technical capability, enterprise's operational performance, and the fit between data and tools, to establish a model and study the correlation between the variables. The results highlight the mediating role of data-tool fit in the relationships between BDA capability and the enterprise's operational performance, which is a major finding that has not been underlined in the extant literature. This study provides valuable insight for operational managers to help them in mobilizing BDA capability for enterprises' operational management and improving operational performance.

Empirical Analysis of the Impact of Industrial Internet Development Environment on Open Green Innovation of Manufacturing Enterprises

This study aims to expand the research perspective from the micro-enterprise level to the regional environment level to identify changes in the regional industrial Internet environment. The development and application of Industrial Internet technologies formed by these changes have spillover effects on Industrial Internet innovation. Sample data from 30 provinces and big cities of China from 2006 to 2018 were used to verify the network externality characteristics of industrial Internet development. The nonlinear impact of environmental factors, such as the proportion of Internet users and intellectual property protection on the open green innovation of manufacturing enterprises, was investigated through the panel threshold model. Meanwhile, the development level of the industrial Internet in eastern and western China is compared and analyzed. This study contributes to existing knowledge and guides practitioners to help manufacturing organizations develop industrial Internet environments.

Environment Sustainability Is a Corporate Social Responsibility: Measuring the Nexus between Sustainable Supply Chain Management, Big Data Analytics Capabilities, and Organizational Performance

Sustainability has profound implications for environmental competitiveness, yet little has been done to study the feasibility of sustainable supply chain management (SSCM) practices as a predictor of organizational performance (operational and environmental performance). By integrating stakeholder theory and dynamic capability theory, this study aims to determine the impact of corporate social responsibility (CSR) on SSCM practices and assess its impact on organizational performance. This research also investigates the role of big data analytical capabilities (BDAC) in mediating the relationship between SSCM practices and organizational performance. The authors collected data online, examined 320 valid responses, and tested research hypotheses. The findings suggest that CSR (both internal and external CSR) positively promotes SSCM practices and contributes to expanding dynamic capacity theory in the context of BDA capabilities. BDAC is also a key mediator between SSCM practices and organizational performance. These results contribute to and improve the research on stakeholder theory and SSCM practice and provide a new perspective for scholars to further study this issue.

Impact of big data analytics on sales performance in pharmaceutical organizations: The role of customer relationship management capabilities

In this era of technology development, every business wants to equip its salesforce with a sustainable salesforce automation system to improve sales performance and customer relationship management (CRM) capabilities. This study investigates the impact of big data analytics (BDA) on CRM capabilities and the sales performance of pharmaceutical organizations. A research model was tested based on 416 valid responses collected from pharmaceutical companies through a structured questionnaire. Structural equation modeling (SEM) was employed using Smart-PLS3 to confirm the contribution of BDA to improving CRM capabilities and sales performance. The study finds that individual characteristics such as self-efficacy, playfulness, and social norms, along with organizational characteristics such as voluntariness, user involvement, user participation, and management support, are positive predictors of salesforce perception of BDA. This positive perception of BDA increased the person-technology fit in the salesforce, which ultimately increased the CRM capabilities and sales performance.