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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.

To identify the factors that influence adoption of e‐voting services by citizens between the ages of 18‐24.

This study uses Carter and Belanger's (2005) model of e‐government adoption to assess young voters' intention to use an online voting system. The study integrates constructs from technology acceptance, diffusion of innovation, and web trust models. A survey is administered to 208 young voters. The data is analyzed using multiple regression analysis.

Results indicate that user perceptions of compatibility, usefulness, and trust significantly impact their intention to use an electronic‐voting system. The model explains 76 percent of the variance in young voters' intention to use an e‐voting system.

The study only explores the perceptions of one age group. Future studies could use the model to access adoption perceptions of a more diverse pool of citizens.

Government agencies should emphasize the benefits of this electronic service to young voters. If marketed properly, the convenience and compatibility of e‐voting may be influential enough to motivate this normally apathetic demographic to participate in the election process.

This study explores adoption of internet voting by young citizens. An understanding of the factors that influence this demographics' intention to use e‐voting systems can be used to increase voter participation. The findings of this study also lay the foundation for future studies on e‐voting adoption.

E‐government adoption is the focus of many research studies. However, few studies have compared the adoption factors to identify the most salient predictors of e‐government use. The purpose of this paper is to compare popular adoption constructs to identify the most influential.

A survey was administered to elicit citizen perceptions of e‐government services.

The results of stepwise regression indicate perceived usefulness, trust of the internet, previous use of an e‐government service and perceived ease of use all have a significant impact on one's intention to use an e‐government service. Perceived usefulness emerges as the most important factor in predicting e‐government adoption. This factor alone explains 74.8 percent of the variance in intention to use.

In light of these findings, researchers should still explore the role of adoption factors in e‐government diffusion. The proposed model is a robust, yet parsimonious way to explore the key factors that influence technology acceptance. This study includes the adoption perceptions of citizens from one state in the USA. Future studies should overcome this limitation by incorporating citizens from other regions and nations.

This study illustrates the elements of e‐government use that are most salient to citizens. Government agencies should focus on these factors when designing and promoting e‐services.

This study shows that the integration of a technology acceptance model, trust and previous e‐government experience work together to explain a large percentage of variance in intention to use e‐government. In particular, perceived usefulness (aka relative advantage, aka performance expectancy) is the most salient predictor.

The purpose of this paper is to investigate the influence of six determinants on taxpayers' intention to adopt e‐file systems. The proposed model integrates technology adoption factors from the unified theory of acceptance and use of technology (UTAUT) model with personal perceptions on trust, efficacy, and security into one parsimonious yet explanatory model of e‐file adoption.

A survey was administered to 304 US taxpayers to capture their perceptions of e‐filing. The survey was developed using existing scales in the literature. Responses were measured on a seven‐point Likert scale, ranging from 1 (strongly disagree) to 7 (strongly agree). The results were tested using multiple linear regression analysis.

The findings of this research show that theoretical constructs from the UTAUT model are well suited in explaining intentions to use multiple e‐government services. Specifically, the results indicate that three factors from the UTAUT model (performance expectancy, effort expectancy, and social influence) play a significant role in predicting taxpayers' e‐filing intentions. More importantly, the research findings indicate that personal factors (web‐specific self‐efficacy (WSSE) and perceived security control), along with UTAUT factors, have a significant impact on taxpayers' e‐file intentions. The proposed model explains 63.5 percent of the variance in taxpayers' e‐file intentions.

This study contributes to the literature by integrating determinants from the UTAUT model with personal perception factors to explain e‐file adoption. This merging of UTAUT with theories, such as social cognition, that emphasize human perception, is the direction that must be taken by researchers in an effort to understand taxpayers' intentions to adopt e‐file systems. While the proposed model explained 63.5 percent of the variation in e‐file use intention, there are limitations to this research. The participants in this research are not sufficiently diverse in culture, socio‐economic level, etc. and 89 percent of the research participants are Caucasian. In addition, the participants were recruited from limited geographical locations. The strength of the model should be validated using more diverse research participants that will increase the variation in the data collected.

The paper presents a parsimonious, yet integrated, model of e‐file diffusion. The integration of adoption factors with personal perceptions of trust, efficacy, and security represents a significant step forward in explaining e‐file adoption.

The purpose of this viewpoint is to identify the potential for future research on information privacy and e‐government.

Using a concise review of major privacy studies, the paper presents an overview of information privacy research in e‐government. Using privacy calculus, it proposes seven factors that have an impact on one's concern for information privacy (CFIP) when disclosing information to or completing a transaction with the government.

The model posits that seven factors – perceived internet privacy risk, collection, error, secondary use, improper access, reputation, and third party certificate – have a significant impact on CFIP.

This viewpoint provides a timely discussion on information privacy and e‐government. It also provides several suggestions for future research in this area. This viewpoint is a call for research on information privacy and e‐government.