Urbanization, industrialization and SO2 emissions in China: does the innovation ability of cities matter for air quality?

This paper aims to detail the relationships between urbanization, industrialization, the innovation ability of cities and local air quality in 284 cities in China using annual data. For the empirical outputs, the panel quantile regression analysis, which considers the heterogeneous nature of the data set, is employed. Initial findings indicate that (i) urbanization and industrialization negatively affect local air quality. (ii) Innovation capability of cities has a direct and improving impact on local air quality. Then, the paper estimates the moderating role of cities' ability to innovate in the polluting effect of urbanization and industrialization on local air quality. Remarkably, empirical evidence indicates that (iii) the innovation ability of cities also moderates the polluting impact of urbanization and industrialization on local air pollution. Based on the findings, the paper confirms the importance of both direct and moderator effects of the innovative environment in cities in tackling air pollution.

Employees' perception of robots and robot-induced unemployment in hospitality industry under COVID-19 pandemic.

The impact of the pandemic is driving the recent upsurge in service automation and the adoption of service robots in the hospitality industry. As service paradigm and customer expectations shift from conventional customized and personalized services towards a digitalized service environment, such customer orientation may favor using service robots at scales that could render service employees redundant. This study aims to answer the above question by investigating service employees' perceptions of service robots. Data solicited from 405 service employees in the United States of America via Amazon's MTurk were analyzed using structural equation modeling. The result revealed that employees' awareness of adopting and using service robots significantly impacts their perception of robot-induced unemployment. Further, results indicated that the perception of robots' social skills significantly influences service employees' perception of robot-induced unemployment. Employee status was found to moderate the relationships mentioned above. Specifically, entry-level employees perceive the unemployment risk more than managers.