While the transportation sector is one of largest economic growth drivers for many countries, the adverse impacts of transportation on air quality are also well-noted, especially in developing countries. Carbon dioxide (CO₂) emissions are one of the direct results of a transportation sector powered by burning fossil-based fuels. Detailed knowledge of CO₂ emissions produced by the transportation sectors in various countries is essential for these countries to revise their future energy investments and policies. In this framework, three machine learning algorithms, ordinary least squares regression (OLS), support vector machine (SVM), and gradient boosting regression (GBR), are used to forecast transportation-based CO₂ emissions. Both socioeconomic factors and transportation factors are also included as features in the study. We study the top 30 CO₂ emissions-producing countries, including the Tier 1 group (the top five countries, accounting for 61% of global CO₂ emissions production) and the Tier 2 group (the next 25 countries, accounting for 35% of total CO₂ emissions production). We evaluate our model using four-fold cross-validation and report four frequently used statistical metrics ($R^2$, MAE, rRMSE, and MAPE). Of the three machine learning algorithms, the GBR model with features combining socioeconomic and transportation factors (GBR_ALL) has the best performance, with an $R^2$ value of 0.9943, rRMSE of 0.1165, and MAPE of 0.1408. We also find that both transportation features and socioeconomic features are important for transportation-based CO₂ emission prediction. Transportation features are more important in modeling for 30 countries, while socioeconomic features (especially GDP and population) are more important when modeling for Tier 1 and Tier 2 countries.

Purpose: Message stream advertising (MSA) has become an increasingly popular option for advertising on mobile social media. However, MSA is often avoided by consumers, and this avoidance deserves more research attention. The purpose of this study is therefore to identify the underlying mechanism and key variables that affect consumer avoidance of MSA in the context of mobile social media.Design/methodology/approach: A face-to-face survey was administered to current mobile users of WeChat (N = 438). Structural equation modeling was conducted to test the relationships in the research model.Findings: Results revealed that mobile consumers employ mechanical avoidance methods (i.e. zipping, muting and zapping) against MSA. The findings also demonstrated that advertising intrusiveness (stimulus) is directly linked to negative emotions, perceived entertainment and sense of control (organism), which, in turn, relate to MSA avoidance (response).Originality/value: The study contributes to the MSA avoidance literature by using the stimulus-organism-response model to deepen the understanding of consumers' MSA avoidance on mobile social media, and it suggests important managerial implications for advertising practitioners and platform operators.

Purpose: The COVID-19 pandemic has made contactless services such as those provided by robots increasingly pervasive. Some stores are gradually adopting service robots to sell products, which has not been explored in previous research. This study aims to explore how appearance personification of service robots affects customer decision-making in the product recommendation context.Design/methodology/approach: Based on authentic in-store product recommendation service interactions, an experiment for three simulated scenarios was conducted and data was collected from 338 valid samples.Findings: The results show appearance personification has a positive impact on customer purchase behavior while it has negative impacts on customer decision time and degree of hesitation.Originality/value: This study not only enriches the literature on application scenarios of service robots but also supplements the literature on various customer decision-making variables in the field of service robots. It provides important practical guidance for designing robots to optimize their impact on customer decision-making.

The service quality gap model, which identifies the antecedents of SERVQAUL, reveals that service quality depends on the gap between customer perceived and expected service (Gap 5), which can be caused by other gaps in the service process (Gaps 1–4). The emergence of service robots has affected the quality of services provided; however, little is known about how these impacts happen. Thus, this paper aims to explore the impacts of service robots on service quality by revising the original gap model in the context of robot service. This paper reviews and analyzes the literature related to service robots and develops a revised gap model for robot service by integrating the existing research on the impacts of service robots. By introducing the roles of robots and robot manufacturers into the original gap model, the revised gap model adds three new gaps: Gap 6 (manufacturers’ understanding gap), Gap 7 (technical gap), and Gap 8 (service coordination gap). Based on the revised gap model, the impacts of service robots on service quality are expounded (i.e., Gap 5 can be affected by not only Gaps 1–4 but also Gaps 6–8), and several propositions are introduced. This paper is the first to consider robot manufacturers as part of the service quality gap model, and the first to use a dynamic model to explore the impacts of service robots on service quality. This paper not only supplements the service robot and gap model literature but also provides service companies with a meaningful framework to improve service quality when using robots to provide service.

From the perspective of production structure sustainability, this study investigates the source of innovation power of high-tech enterprises. It proposes that the roundaboutness of production structure is the sustainable driving force for improving innovation capability in high-tech enterprises, and enterprises can improve their innovation capability by absorbing technology from three paths. The proposed model was tested based on the survey data from 315 enterprises in 10 robotics industrial parks in the Yangtze River Delta region of China. The results show that the roundaboutness of production structure has a positive influence on the innovation capability of high-tech enterprises, which is mediated by technology absorption paths (i.e., independent development in high-tech enterprises, technology acquisition in high-tech enterprises and government policy compliance in high-tech enterprises). Meanwhile, the enterprise has different technology absorption path at different position in production structure. The findings have important enlightenment for enterprises to improve market competitiveness, production efficiency and technological innovation capability, and provides a practical basis for better ways of leveraging market and capital and more effective government processes in industrial innovation.