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This study presents a systematic literature review (SLR) of the interdisciplinary literature on drones in last-mile delivery (LMD) to extrapolate pertinent insights from and into the logistics management field.

Rooting their analytical categories in the LMD literature, the authors performed a deductive, theory refinement SLR on 307 interdisciplinary journal articles published during 2015–2022 to integrate this emergent phenomenon into the field.

The authors derived the potentials, challenges and solutions of drone deliveries in relation to 12 LMD criteria dispersed across four stakeholder groups: senders, receivers, regulators and societies. Relationships between these criteria were also identified.

This review contributes to logistics management by offering a current, nuanced and multifaceted discussion of drones' potential to improve the LMD process together with the challenges and solutions involved.

The authors provide logistics managers with a holistic roadmap to help them make informed decisions about adopting drones in their delivery systems. Regulators and society members also gain insights into the prospects, requirements and repercussions of drone deliveries.

This is one of the first SLRs on drone applications in LMD from a logistics management perspective.

To effectively combat the increasing rates of obesity, it is crucial to explore how environmental factors like sidewalk access impact weight-related outcomes. This study aimed to systematically examine the association between sidewalk accessibility and weight-related outcomes.

Databases were searched by keywords for relevant articles, which were published before March 3, 2024, to report the role of neighborhood sidewalk access on weight-related outcomes. The main findings of the selected articles were extracted from eligible studies by two independent reviewers.

A total of 20 out of 33 studies indicated a significant negative relationship between access to sidewalks and weight-related outcomes. Three studies demonstrated an indirect relationship between access to sidewalks and weight-related outcomes by greater access to physical environments. In addition, five studies reported no clear relationship, and three studies reported a significantly positive relationship between access to sidewalks and weight-related outcomes.

In general, people who live in urban areas with better sidewalk access benefit from better weight-related outcomes. Adults showed this correlation more prominently than adolescents and children. Therefore, sidewalks that have a positive effect on physical activity levels could be considered as a preventive measure against obesity.

One of the weight-related outcomes is obesity. Every community faces numerous challenges due to obesity, which adversely affects the quality of life and health. Environmental factors such as access to sidewalks could be associated with body weight due to lifestyle influences.

This study is motivated to provide a theoretically informed, data-driven assessment of the consequences associated with the participation of non-human bots in social accountability movements; specifically, the anti-inequality/anti-corporate #OccupyWallStreet conversation stream on Twitter.

A latent Dirichlet allocation (LDA) topic modeling approach as well as XGBoost machine learning algorithms are applied to a dataset of 9.2 million #OccupyWallStreet tweets in order to analyze not only how the speech patterns of bots differ from other participants but also how bot participation impacts the trajectory of the aggregate social accountability conversation stream. The authors consider two research questions: (1) do bots speak differently than non-bots and (2) does bot participation influence the conversation stream.

The results indicate that bots do speak differently than non-bots and that bots exert both weak form and strong form influence. Bots also steadily become more prevalent. At the same time, the results show that bots also learn from and adapt their speaking patterns to emphasize the topics that are important to non-bots and that non-bots continue to speak about their initial topics.

These findings help improve understanding of the consequences of bot participation within social media-based democratic dialogic processes. The analyses also raise important questions about the increasing importance of apparently nonhuman actors within different spheres of social life.

The current study is the first, to the authors’ knowledge, that uses a theoretically informed Big Data approach to simultaneously consider the micro details and aggregate consequences of bot participation within social media-based dialogic social accountability processes.

This study examined how likeminded Facebook comments with incivility and without supporting evidence influence readers' evaluations of the commenter and online political participation intention. This study also investigated whether the indirect effect of exposure to uncivil comments and comments without evidence on online political participation through evaluations of the commenters is contingent on the strength of partisanship.

The study used a 2 (uncivil comments vs civil comments) × 2 (comments without supporting evidence vs comments with supporting evidence) factorial design with a scenario about reading similar viewpoints about the gun control issue in Facebook comments.

The results showed that compared to exposure to civil agreeing comments, exposure to uncivil likeminded comments resulted in higher levels of negative evaluations of the commenters, which in turn decreased willingness to participate in political activities online. Exposure to comments without evidence led to more negative evaluations of the commenters, but it did not significantly influence online political participation. In addition, the strength of partisanship did not significantly moderate the indirect effect of exposure to uncivil comments and comments without evidence on online political participation through evaluations of the commenters.

Although previous studies have demonstrated the significant effects of incivility and reasoned argument, little is known about whether and how people evaluate ingroup members' comments that are uncivil and lacking reasoned arguments. Most of these studies have examined incivility in political contexts, but few have extended the context to the effect of likeminded comments, especially when the comments are uncivil and lack supporting evidence. The current study aims to fill this gap in the literature.

From book challenges to anti–critical race theory and anti-lesbian, gay, bisexual, transgender, queer and questioning legislation, US English teachers have been on the receiving end of a considerable amount of far-right conservative pushback. This study aims to explore the effects of conservative pushback on individual English teachers and their classroom practice. What pushbacks have individual English teachers faced? How has pushback impacted their teaching? What strategies have they developed for navigating pushback?

This qualitative study explores secondary English teachers’ reported experiences with conservative backlash as reported in 15 semi-structured interviews conducted between May 2022 and August 2023.

Participants reported feeling the pressure of increased levels of pushback, and many reported censoring their book selections to avoid additional public scrutiny. At the same time, they also described a range of strategies they have developed for protecting themselves and their practice, such as codifying curriculum, increasing transparency, formalizing review processes for challenging books and strengthening their resolve to resist.

This study offers a timely window on a pressing problem affecting the daily practice of English teachers in the USA.

The purpose of this study is to develop an intelligent tutoring system (ITS) for programming learning based on information tutoring feedback (ITF) to provide real-time guidance and feedback to self-directed learners during programming problem-solving and to improve learners’ computational thinking.

By analyzing the mechanism of action of ITF on the development of computational thinking, an ITF strategy and corresponding ITS acting on the whole process of programming problem-solving were developed to realize the evaluation of programming problem-solving ideas based on program logic. On the one hand, a lexical and syntactic analysis of the programming problem solutions input by the learners is performed and presented with a tree-like structure. On the other hand, by comparing multiple algorithms, it is implemented to compare the programming problem solutions entered by the learners with the answers and analyze the gaps to give them back to the learners to promote the improvement of their computational thinking.

This study clarifies the mechanism of the role of ITF-based ITS in the computational thinking development process. Results indicated that the ITS designed in this study is effective in promoting students’ computational thinking, especially for low-level learners. It also helped to improve students’ learning motivation, and reducing cognitive load, while there’s no significant difference among learners of different levels.

This study developed an ITS based on ITF to address the problem of learners’ difficulty in obtaining real-time guidance in the current programming problem-solving-based computational thinking development, providing a good aid for college students’ independent programming learning.

This study aims to elucidate the dynamics of social media discourse during global health events, specifically investigating how users across different platforms perceive, react to and engage with information concerning such crises.

A mixed-method approach was employed, combining both quantitative and qualitative data collection. Initially, thematic analysis was applied to a data set of social media posts across four major platforms over a 12-month period. This was followed by sentiment analysis to discern the predominant emotions embedded within these communications. Statistical tools were used to validate findings, ensuring robustness in the results.

The results showcased discernible thematic and emotional disparities across platforms. While some platforms leaned toward factual information dissemination, others were rife with user sentiments, anecdotes and personal experiences. Overall, a global sense of concern was evident, but the ways in which this concern manifested varied significantly between platforms.

The primary limitation is the potential non-representativeness of the sample, as only four major social media platforms were considered. Future studies might expand the scope to include emerging platforms or non-English language platforms. Additionally, the rapidly evolving nature of social media discourse implies that findings might be time-bound, necessitating periodic follow-up studies.

Understanding the nature of discourse on various platforms can guide health organizations, policymakers and communicators in tailoring their messages. Recognizing where factual information is required, versus where sentiment and personal stories resonate, can enhance the efficacy of public health communication strategies.

The study underscores the societal reliance on social media for information during crises. Recognizing the different ways in which communities engage with, and are influenced by, platform-specific discourse can help in fostering a more informed and empathetic society, better equipped to handle global challenges.

This research is among the first to offer a comprehensive, cross-platform analysis of social media discourse during a global health event. By comparing user engagement across platforms, it provides unique insights into the multifaceted nature of public sentiment and information dissemination during crises.