Group behavior in social media: Antecedents of initial trust formation

Facebook and drug driving: Does online sharing work against road safety countermeasures?

INTRODUCTION

Enhancements to technology have transformed several aspects of road safety, communication, and connectivity. At the intersection of these, scholars have begun to speculate whether certain aspects of technology may provide motorists ways to engage in illegal and dangerous driving behaviors without consequences. Police traffic operations such as Roadside Drug Testing are intended to occur "anywhere, anytime" to communicate to motorists that they should not risk offending. A potential challenge for road safety is the emergence of Facebook police location pages and groups, where users can share the locations of police operations.

METHOD

In this study, the authors examined two Facebook police location groups and three pages from Queensland (Australia) and conducted a content analysis of posts related to Roadside Drug Testing operations and a thematic analysis of comments on these posts. A total of 282 posts related to Roadside Drug Testing were identified between February and April 2021, with 1,823 comments made.

RESULTS

The findings suggest that some users: (a) had lived experiences of avoiding punishment for drug driving; (b) remain unaware of how long to wait between drug consumption and driving; (c) perceived Roadside Drug Testing operations to be for the purpose of revenue raising; and (d) changed their driving behavior upon seeing an operation.

CONCLUSIONS

These findings suggest consideration is needed regarding the responsibility of both Facebook, and the government, in allowing groups and pages to exist that undermine law enforcement.

PRACTICAL APPLICATIONS

Regarding practice, the comments suggest that greater education is needed regarding "safe" times to drive after drug taking.

Influence of Emojis on Online Trust Among College Students

Emojis are increasingly used in online communication and expression, however, most previous studies have focused on describing this phenomenon, but less on how it affects interpersonal trust relationships. Therefore, this study examines the effect of emojis on online interpersonal trust among college students through three experiments. A total of 62 college students were recruited for Experiment 1. The results demonstrated that positive emoji () improved the level of trust of trustors in the trust game [t(60) = –2.79, p = 0.007], whereas that of the control group exerted no effect on the initial level of online trust among college students. Then, 74 college students were selected for Experiment 2. The results indicated no significant differences between the experiment and control groups in terms of the influence of negative emojis () on initial online trust using. A joint analysis (via ANOVA) of Experiments 1 and 2 illustrated that the type of emoji exerted a significant effect [F(2,96) = 3.96, p = 0.02, η² = 0.08] on college students’ online trust. Finally, we recruited 111 participants for Experiment 3 to explore the role of emojis on online trust among acquaintances. The results suggested that the individual propensity to trust plays a moderate role in the relationship between emojis and online trust among acquaintances. That is, emojis influenced interpersonal trust among acquaintances only if the level of propensity to trust, is low.

Lockdown and sustainability: An effective model of information and communication technology

Covid-19, a corona virus, has maintained its momentum in spreading among communities. In this context of social crisis, this study seeks to identify the reasons for the partial failure to fulfill the intended goal of lockdown, and to formulate an inclusive behavioral model reflecting comprehensive human behavior and social psychology. In order to answer the research questions, this study has conducted extensive interviews among individuals who were targets of the lockdown system. From this exploratory and qualitative investigation, researchers have recognized four paradigms as the key to understanding human behavior and social psychology in violating lockdown as a social isolation system during this period of crisis. The identified parameters depicting social behavior are: Derogation and Argument (SDA), Tangible Need and Deficiency (TND), Intangible Desire and Expectancy (IDE), and Evaluation of Benefit and Loss (UBL). Finally, as a comprehensive guideline, a grounded theory of the social behavior 'paradigm for lockdown violation (PLV)' is explored as the reason for the violation of the social system.

Determinant Factors Influencing Thai Tourists’ Intentions to Use Social Media for Travel Planning

Currently, information technology plays an important role in the tourism industry. Tourists like to use social media more widely for their travel planning, which affects the high competition between entrepreneurs while facing the challenge of improving the quality of data services provided through social media. This study developed a model of the relationships between structure factors that affect intentions to use social media for travel planning. The said model integrates the technology acceptance model (TAM) theory, the theory of planned behavior (TPB), and trust and satisfaction factors. In reference to a survey of 1333 samples from domestic tourists who used social media through their smartphone, it was found that satisfaction, attitudes, subjective norms, and perception can be used to control behavior, with a positively direct effect on intentions of use. In addition, we examined the indirect effect on intentions of use. Perceived ease of use, perceived usefulness, and subjective norms had a positively significant indirect effect on intentions of use. These research results allow tourism entrepreneurs to precisely understand the stimulating factors of tourists’ intentions to use social media for travel-planning decisions, which will help lead to the development of tourism-marketing strategies and the support of sustainable competition.

Using Data Sciences in Digital Marketing: Framework, methods, and performance metrics

In the last decade, the use of Data Sciences, which facilitate decision-making and extraction of actionable insights and knowledge from large datasets in the digital marketing environment, has remarkably increased. However, despite these advances, relevant evidence on the measures to improve the management of Data Sciences in digital marketing remains scarce. To bridge this gap in the literature, the present study aims to review (i) methods of analysis, (ii) uses, and (iii) performance metrics based on Data Sciences as used in digital marketing techniques and strategies. To this end, a comprehensive literature review of major scientific contributions made so far in this research area is undertaken. The results present a holistic overview of the main applications of Data Sciences to digital marketing and generate insights related to the creation of innovative Data Mining and knowledge discovery techniques. Important theoretical implications are discussed, and a list of topics is offered for further research in this field. The review concludes with formulating recommendations on the development of digital marketing strategies for businesses, marketers, and non-technical researchers and with an outline of directions of further research on innovative Data Mining and knowledge discovery applications.

Feeling Positive About Reopening? New Normal Scenarios From COVID-19 US Reopen Sentiment Analytics

The Coronavirus pandemic has created complex challenges and adverse circumstances. This research identifies public sentiment amidst problematic socioeconomic consequences of the lockdown, and explores ensuing four potential public sentiment associated scenarios. The severity and brutality of COVID-19 have led to the development of extreme feelings, and emotional and mental healthcare challenges. This research focuses on emotional consequences - the presence of extreme fear, confusion and volatile sentiments, mixed along with trust and anticipation. It is necessary to gauge dominant public sentiment trends for effective decisions and policies. This study analyzes public sentiment using Twitter Data, time-aligned to the COVID-19 reopening debate, to identify dominant sentiment trends associated with the push to reopen the economy. Present research uses textual analytics methodologies to analyze public sentiment support for two potential divergent scenarios - an early opening and a delayed opening, and consequences of each. Present research concludes on the basis of textual data analytics, including textual data visualization and statistical validation, that tweets data from American Twitter users shows more positive sentiment support, than negative, for reopening the US economy. This research develops a novel sentiment polarity based public sentiment scenarios (PSS) framework, which will remain useful for future crises analysis, well beyond COVID-19. With additional validation, this research stream could present valuable time sensitive opportunities for state governments, the federal government, corporations and societal leaders to guide local and regional communities, and the nation into a successful new normal future.