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This research analyses the impact of customers and government regulations on the implementation of socio-environmental practices in certifying organic agricultural products. It explores the dyad’s relationship between the focal company and its suppliers in the application of socio-environmental practices.

This study uses a quantitative methodology through a survey approach, with a sample of 206 agro-extractivists from the acai berry supply chain. The data are evaluated using regression analysis.

The main results reveal that customer pressure positively influences the implementation of social and environmental practices, but suggest a non-significant relationship between government regulations and the impact on environmental practices implementation. Social and environmental practices are positively related to operational performance. A moderating effect of organic certification is found in the relationship between customer pressure and the application of environmental practices.

The main contributions are exploring the use of socio-environmental practices in an emerging economy and organic certification as a moderating variable, revealing an “institutional void” that may hamper the enforcement of government regulations.

Many librarians are asked questions about copyright and intellectual property. They may be expected to advise on copyright or provide copyright education as part of their duties. Others may be “voluntold” to take on copyright, which may come as an unexpected addition to their workload. This case study provides suggestions for librarians to increase their copyright knowledge and create copyright education programs.

This case study showcases two copyright education programs created by a librarian in a college and another in a university. The librarians collaborated to learn more about the state of copyright education within academic libraries and explore their commonalities and differences. This case study introduces two copyright education programs and summarizes the state of copyright education within library and information science (LIS) and academic libraries.

The following themes within the two copyright education programs were identified through a case study: the complexity of copyright, the engagement (or lack thereof) across a college or university, the necessity of including copyright in information literacy instruction and the calls for professional development with copyright.

This case study covers two differing institutions so its conclusions may not be applicable to all libraries or educational settings.

Many individuals who are in disciplines or occupations that regularly work with copyright may generate ideas for creating and providing continuing education within their organizations.

Library or education professionals can use the case study’s conclusions to inform and support their ongoing work with teaching and learning about copyright and intellectual property. By doing so, they can better support their students, faculty and institutions.

This paper aims to facilitate the research and development of resilient navigation approaches, explore the robustness of adversarial training to different interferences and promote their practical applications in real complex environments.

In this paper, the authors first summarize the real accidents of self-driving cars and develop a set of methods to simulate challenging scenarios by introducing simulated disturbances and attacks into the input sensor data. Then a robust and transferable adversarial training approach is proposed to improve the performance and resilience of current navigation models, followed by a multi-modality fusion-based end-to-end navigation network to demonstrate real-world performance of the methods. In addition, an augmented self-driving simulator with designed evaluation metrics is built to evaluate navigation models.

Synthetical experiments in simulator demonstrate the robustness and transferability of the proposed adversarial training strategy. The simulation function flow can also be used for promoting any robust perception or navigation researches. Then a multi-modality fusion-based navigation framework is proposed as a light-weight model to evaluate the adversarial training method in real-world.

The adversarial training approach provides a transferable and robust enhancement for navigation models both in simulation and real-world.

This study aims to examine the role of hope and hate in political leaders’ messages in influencing liberals versus conservatives’ social-distancing behavior during the COVID-19 pandemic. Given the increasing political partisanship across the world today, using the appropriate message framing has important implications for social and public policy.

The authors use two Natural Language Processing (NLP) methods – a pretrained package (HateSonar) and a classifier built to implement our supervised neural network-based model architecture using RoBERTa – to analyze 61,466 tweets by each US state’s governor and two senators with the goal of examining the association between message factors invoking hate and hope and increased or decreased social distancing from March to May 2020. The authors examine individuals’ social-distancing behaviors (the amount of nonessential driving undertaken) using data from 3,047 US counties between March 13 and May 31, 2020, as reported by Google COVID-19 Community Mobility Reports and the New York Times repository of COVID-19 data.

The results show that for conservative state leaders, the use of hate increases nonessential driving of state residents. However, when these leaders use hope in their speech, nonessential driving of state residents decreases. For liberal state leaders, the use of hate displays a directionally different result as compared to their conservative counterparts.

Amid the emergence of new analytic techniques and novel data sources, the findings demonstrate that the use of global positioning systems data and social media analysis can provide valuable and precise insights into individual behavior. They also contribute to the literature on political ideology and emotion by demonstrating the use of specific emotion appeals in targeting specific consumer segments based on their political ideology.

The findings have significant implications for policymakers and public health officials regarding the importance of considering partisanship when developing and implementing public health policies. As partisanship continues to increase, applying the appropriate emotion appeal in messages will become increasingly crucial. The findings can help marketers and policymakers develop more effective social marketing campaigns by tailoring specific appeals given the political identity of the consumer.

Using Neural NLP methods, this study identifies the specific factors linking social media messaging from political leaders and increased compliance with health directives in a partisan population.