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This study aims to address the following objectives: determine the pro-environmental behavior of students and professors engaged in the course Sustainable Tourism; determine the pro-environmental behavior component of the professor that has the highest influence on the pro-environmental behavior of students; and formulate a model describing the components of the pro-environmental behavior of the professor with significant influence on the pro-environmental behavior of students. It also evaluates the norm-activation-theory-aligned pro-environmental behavior of the course's instructor and the students taking the course after they participated in the course Sustainable Tourism.

A correlational analysis using the partial least squares structural equation modeling approach was conducted in the survey results to determine the specific components of the professor's pro-environmental behavior that influenced the students' pro-environmental behavior.

Situational responsibility, efficacy and denial of responsibility were proven more influential in shaping students' pro-environmental behavior. This implies that students learn and adopt a pro-environmental orientation through role modeling and practical application, not by knowledge acquisition.

This could improve the body of knowledge on pro-environmental behavior by analyzing the reciprocal relationships between the person who delivers the course that instills pro-environmental behavior and students who learn from their professors using the norm activation theory.

Social media platforms are highly visible platforms, so politicians try to maximize their benefits from their use, especially during election campaigns. On the other side, people express their views and sentiments toward politicians and political issues on social media, thus enabling them to observe their online political behavior. Therefore, this study aims to investigate user reactions on social media during the 2016 US presidential campaign to decide which candidate invoked stronger emotions on social media.

For testing the proposed hypotheses regarding emotional reactions to social media content during the 2016 presidential campaign, regression analysis was used to analyze a data set that consists of Trump’s 996 posts and Clinton’s 1,253 posts on Facebook. The proposed regression models are based on viral (likes, shares, comments) and emotional Facebook reactions (Angry, Haha, Sad, Surprise, Wow) as well as Russell’s valence, arousal, dominance (VAD) circumplex model for valence, arousal and dominance.

The results of regression analysis indicate how Facebook users felt about both presidential candidates. For Clinton’s page, both positive and negative content are equally liked, while Trump’s followers prefer funny and positive emotions. For both candidates, positive and negative content influences the number of comments. Trump’s followers mostly share positive content and the content that makes them angry, while Clinton’s followers share any content that does not make them angry. Based on VAD analysis, less dominant content, with high arousal and more positive emotions, is more liked on Trump’s page, where valence is a significant predictor for commenting and sharing. More positive content is more liked on Clinton’s page, where both positive and negative emotions with low arousal are correlated to commenting and sharing of posts.

Building on an empirical data set from Facebook, this study shows how differently the presidential candidates communicated on social media during the 2016 election campaign. According to the findings, Trump used a hard campaign strategy, while Clinton used a soft strategy.

The aim of this paper is to examine trust, commitment and social sustainability (SS) in agri-food supply chains (AFSCs) in an underexplored context, an emerging country. This adds to the literature on the benefits of trust and commitment in AFSCs.

Within four counties in Kenya — Nairobi, Machakos, Kiambu and Kajiado — 85 abattoirs and 164 traders were surveyed as part of data collection. Hypotheses were proposed, and partial least squares structural equation modelling (PLS-SEM) was used to test them.

The findings indicate that trust (benevolent and credibility trust) as well as commitment (affective and continuance commitment) have a positive direct relationship with SS.

The study underlines how important trust and commitment are in improving the social well-being of AFSC actors. They enhance collaboration which empowers them to improve social welfare of the supply chain.

The study is one of the first to examine the effect of trust and commitment on SS within AFSC, a novel contribution to SS literature. It differentiates between types of trust (benevolent and credibility trust) and commitment (affective and continuance commitment), giving a clearer understanding of how each of the variables impact SS. The findings offer new insights into the relationship between trust and commitment in improving social sustainable practices in AFSCs.