Streamers are the individuals at the core of e-commerce live streaming (ELS), which is a successful influencer marketing strategy. However, a systematic profile of ELS streamers is lacking. This study aims to investigate the roles and characteristics of ELS streamers, as well as their matching relationships, by applying the grounded theory method to analyze semi-structured interviews of 96 consumers. Eight characteristics of ELS streamers (expertise, attractiveness, credibility, interactivity, popularity, price support, affinity, and responsiveness) are identified and further classified into four roles (opinion leader, spokesperson, interactive friend, and salesperson). This study is the first to create a role set for ELS streamers and the first to systematically elucidate the roles of ELS streamers and their corresponding characteristics. It not only supplements e-commerce, influencer marketing, and role theory research by identifying new characteristics and roles of ELS streamers, but it also provides a clear framework practitioners can use to train streamers.

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: Due to customers' extensive avoidance behavior, social commerce may be less successful than anticipated. This study investigates the underlying mechanism and antecedents that influence customers' avoidance of peer-generated advertisements.Design/methodology/approach: Based on the general framework of avoidance behavior, we propose a theoretical model for the context of a mobile social network, with tie strength as the user-related factor and violation of shared language, advertisement relevance and information overload as contextual variables. Using survey data collected from 334 customers on WeChat, we empirically examine the research model and hypotheses.Findings: Tie strength and advertisement relevance are negatively associated with avoidance behavior, whereas information overload and violation of shared language have significantly positive effects. Furthermore, tie strength weakens the negative relationship between violation of shared language and avoidance behavior but strengthens the positive relationship between advertisement relevance and avoidance behavior.Originality/value: The findings extend understanding of advertisement avoidance behavior and can guide practitioners' improvement of advertising efficiency in mobile social networks.

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.