Sci-Fi Fandoms in the Digital Age

This chapter discusses the evolution of the Doctor Who, Star Wars, and Star Trek fandoms from their beginnings to their current releases. These brief histories highlight how fans communicated with each other before social media and how those communications changed with the advent of the internet and social media. The dynamics of online groups, individual behavior in online groups, and the life cycle of a group are all discussed before moving onto trolling and the spectrum of online incivility. Overall, most of the trolling that occurs in sci-fi fandoms comes from devotion to the franchise rather than from the desire to be divisive or negative. However, some online incivility is solely guided by sexism, racism, and the desire to sow social discord. Two examples of sexist and racist fan behavior from Star Wars: The Last Jedi illustrates the different motivations of fandom trolls as well as ways to respond. Although every fandom is different, group behavior is predictable thus insights from these iconic sci-fi fandoms can be applied to many different fandoms.

Assessing individual behavior towards adoption of telemedicine application during COVID-19 pandemic: evidence from emerging market

PurposeThis study attempts to gain insight into what factors influence individual behavior towards the adoption of telemedicine application during coronavirus disease 2019 (COVID-19) pandemic. The research model incorporates two well-known theories namely the extended unified theory of acceptance and use of technology (UTAUT2) and DeLone and McLean information success model to examine individual behavior towards the adoption of telemedicine application.Design/methodology/approachThe research design of this study is based on quantitative research approach. During research survey, 350 valid responses were received from Pakistani citizens and examined to understand citizen's behavior towards the adoption of telemedicine applications. The research model was empirically tested with the latest statistical approach namely variance-based structural equation modeling (VB-SEM).FindingsThe results of the structural equation modeling have revealed that altogether performance expectancy, social influence, effort expectancy, facilitating condition, habit, hedonic motivation, price values, information quality, system quality and service quality explained 77.9% variance in determining user behavior towards adoption of telemedicine application. The predictive relevance of the research model was found substantial in measuring user behavior to adopt telemedicine applications. The research framework is further extended with moderating role of perceived severity between the relationship of user intention and actual usage behavior. Results confirmed that the positive relationship between intention to adopt telemedicine health application and usage behavior will be stronger when perceived severity is higher.Practical implicationsTheoretically, this study integrates extended UTAUT2 and DeLone and McLean information success model and contributes to e-health literature. Practically, this research suggests that by improving user performance expectancy and effort expectancy, managers and healthcare professionals can boost user confidence towards the adoption of telemedicine applications.Originality/valueThis study is unique as it integrates the extended UTAUT2 with DeLone and McLean information success model and perceived severity to investigate user behavior towards adoption of telemedicine application during COVID-19 pandemic. Additionally, the integration of theories contributes to information system literature in the context of the adoption of telemedicine applications.

Applying Behavioral and Physiological Measures to Assess the Relative Impact of the Prolonged COVID-19 Pandemic Closure on Two Mammal Species at the Oregon Zoo: Cheetah (A. jubatus) and Giraffe (G. c. reticulata and G. c. tippelskirchii)

The effect of visitor presence on zoo animals has been explored in numerous studies over the past two decades. However, the opportunities for observations without visitors have been very limited at most institutions. In 2020, the Oregon Zoo was closed, in response to the global SARS-CoV-2 (COVID-19) pandemic, from 15 March 2020 to 12 July 2020, resulting in approximately four consecutive months without visitor presence. This study aimed to quantify potential behavioral and hormonal changes expressed during two transition periods in zoo visitor attendance: the initial time period before and after closure in March 2020 and time before and after reopening in July 2020. Fecal glucocorticoid metabolite (fGM) concentrations of resident giraffes (n = 2) and cheetahs (n = 2) were tracked using enzyme immunoassay (EIA) analyses. Average fGM concentrations during the two transition periods were compared using a two-way mixed ANOVA. Additionally, twice-weekly scan sampling was used to quantify behavioral observations across the transitions, which were analyzed as individual behavior proportions. Individual behavior proportions were compared across the Zoo’s opening status and time of day using Kruskal–Wallis (H) tests. The results of our analyses showed the following outcomes: (1) significant increases in fGM concentrations for cheetahs and giraffes between the transition periods but not within them; (2) a significant increase in time spent ‘not visible’ in the cheetahs in the second transition period; and (3) increased vigilance behaviors in the giraffes immediately after the Zoo’s closure. However, the changes observed in fGM concentrations may be more strongly correlated with concomitant social changes (giraffes) and some medical events (cheetahs) rather than with the Zoo’s opening status. Nevertheless, this study was able to quantify differences in behavioral frequencies and fGM concentration in cheetahs and giraffes at the Oregon Zoo during the times of transition between visitor’s presence and absence. The results indicate that, while there was a possible, but relatively minor impact of the presence and absence of visitors on some behaviors, the differences observed in fGM concentration may have been more affected by some of the concomitant social changes and medical events that happened during the same period than by the presence or absence of visitors.

Sequencing of multi-robot behaviors using reinforcement learning

Given a collection of parameterized multi-robot controllers associated with individual behaviors designed for particular tasks, this paper considers the problem of how to sequence and instantiate the behaviors for the purpose of completing a more complex, overarching mission. In addition, uncertainties about the environment or even the mission specifications may require the robots to learn, in a cooperative manner, how best to sequence the behaviors. In this paper, we approach this problem by using reinforcement learning to approximate the solution to the computationally intractable sequencing problem, combined with an online gradient descent approach to selecting the individual behavior parameters, while the transitions among behaviors are triggered automatically when the behaviors have reached a desired performance level relative to a task performance cost. To illustrate the effectiveness of the proposed method, it is implemented on a team of differential-drive robots for solving two different missions, namely, convoy protection and object manipulation.

Norm-Based Governance for Severe Collective Action Problems: Lessons from Climate Change and COVID-19

The world has surpassed three million deaths from COVID-19, and faces potentially catastrophic tipping points in the global climate system. Despite the urgency, governments have struggled to address either problem. In this paper, we argue that COVID-19 and anthropogenic climate change (ACC) are critical examples of an emerging type of governance challenge: severe collective action problems that require significant individual behavior change under conditions of hyper-partisanship and scientific misinformation. Building on foundational political science work demonstrating the potential for norms (or informal rules of behavior) to solve collective action problems, we analyze more recent work on norms from neighboring disciplines to offer novel recommendations for more difficult challenges like COVID-19 and ACC. Key insights include more attention to 1) norm-based messaging strategies that appeal to individuals across the ideological spectrum or that reframe collective action as consistent with resistant subgroups’ pre-existing values, 2) messages that emphasize both the prevalence and the social desirability of individual behaviors required to address these challenges, 3) careful use of public policies and incentives that make individual behavior change easier without threatening norm internalization, and 4) greater attention to epistemic norms governing trust in different information sources. We conclude by pointing out that COVID-19 and climate change are likely harbingers of other polarized collective action problems that governments will face in the future. By connecting work on norms and political governance with a broader, interdisciplinary literature on norm psychology, motivation, and behavior change, we aim to improve the ability of political scientists and policymakers to respond to these and future collective action challenges.

Life-Areas and How to Estimate Greenhouse Gas Emission Footprints

To enjoy a fulfilling life, a person needs six fundamental life requirements to be met. These six requirements or “life-areas” are housing, mobility, consumption of goods (e.g., clothing), diet, other activities (entertainment), and information. In the beginning of this chapter, a top-down estimate of Austrian consumption-based emissions in each life-area is presented. These are organized into segments that may be easily reduced by changing individual behavior and those segments that are fundamental aspects of our society. The remainder of this chapter discusses how to estimate the greenhouse gas (GHG) output. There is a trade-off between accuracy and level of detail, and the need to combine bottom-up survey results with the top-down national emissions inventory. How these trade-offs may be handled is demonstrated using a practical example.