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The COVID-19 pandemic dramatically altered daily life for Gen Z. The purpose of this study was to examine parental perceptions of the pandemic’s effects on their children’s peer relationships. As children sought peer connection during the pandemic, technology usage soared. The second purpose of the current study was to assess how greater time on social media affected adjustment among Gen Z and whether this effect was mediated by experiences of cyber victimization.

In total, 250 U.S. parent-child dyads participated in the study. Parents reported on their children’s social media use and described how they believed the pandemic affected their children’s peer relationships. Child participants were transitioning to middle school and reported on cyber victimization and adjustment.

Thematic analysis of parental reflections revealed three themes: children spent more time online since the onset of the pandemic, there were negative implications of increased time online and there were positive and protective implications of being online. Analysis also indicated significant indirect effects of social media use on internalizing and externalizing problems through victimization.

Parents reported Gen Z continues to use electronic forms of communication and social media at high rates even after pandemic-related restrictions eased with some suggesting that their children prefer digital over face-to-face communication because they have become accustomed to this way of connecting and may find it easier than in-person interaction. Current findings highlight concerns about this increased time online as social media use negatively affected adjustment via cyber victimization.

This conceptual, multi-voiced paper aims to collectively explore and theorize family entrepreneuring, which is a research stream dedicated to investigating the emergence and becoming of entrepreneurial phenomena in business families and family firms.

Because of the novelty of this research stream, the authors asked 20 scholars in entrepreneurship and family business to reflect on topics, methods and issues that should be addressed to move this field forward.

Authors highlight key challenges and point to new research directions for understanding family entrepreneuring in relation to issues such as agency, processualism and context.

This study offers a compilation of multiple perspectives and leverage recent developments in the fields of entrepreneurship and family business to advance research on family entrepreneuring.

Companies are adopting innovative methods for responsiveness and efficiency in the public transport sector. The implementation of air-taxi services (ATS) in the transport sector is a move in this direction. Air taxis have a two-pronged advantage as they can reduce travel times by avoiding traffic congestion and have the potential to reduce carbon footprint compared to traditional modes of public transportation. Many companies worldwide are developing and testing ATS for practical applications. However, many factors may play a significant role in adopting ATS in the transport sector. This paper attempts to unearth such critical success factors (CSFs) and establish the interrelationships between these factors.

Fifteen CSFs were identified by systematically reviewing the literature and taking experts' input. An integrated multi-criteria decision-making (MCDM) technique, Decision-Making Trial and Evaluation Laboratory-Analytic Network Process (DEMATEL-ANP [DANP]) was used to envisage the causal relationships between the identified CSF.

The results reveal that Govt Regulations (GOR), Skilled Workforce (SKF) and Conductive Research Environment (CRE) are the most influential factors that impact the adoption of ATS in the transport sector.

The research implications of these findings will help practitioners and policymakers effectively implement ATS in the public transportation sector.

This is the first kind of study that identifies and explores the different CSFs for ATS implementation in public transportation. The CSFs are evaluated with the help of a framework built with inputs from logistics experts. The study recognizes the CSFs for ATS implementation and provides a foundation for future research and smooth adoption of ATS.

Guided by stimulus-organism-response (SOR) theory, this study analyzed the user-generated content (UGC) produced by attendees from six anime conventions in the USA.

A total of 739 online reviews and 1,932 photos were collected from the social platforms of six large anime conventions in the USA (Yelp and Facebook), and the study employed thematic analysis and image analysis to analyze the collected UGCs.

The findings revealed eight main themes (i.e. ambient and space, customers, service and products, sign and symbol, social density, emotional status, motivation, and behavior intention) and 32 subthemes across the three dimensions of SOR theory. Leveraging the power of cutting-edge image analysis, the image labels obtained from the analysis contributed to the creation of network clusters. The result of the image analysis also continued consistently with the thematic analysis result, which reflected SOR theory.

Theoretically, the study applied SOR theory and blended thematic and image analyses to gain a comprehensive understanding of anime convention attendees’ experience and categorized the attendees’ emotional status as positive or negative to reflect their overall evaluation. Practically, this study highlighted some complaints from attendees and provided suggestions for operators. However, the study focused only on large anime conventions in the USA; future studies should compare attendees’ experiences with small and large conventions or anime conventions worldwide.

The study utilized UGCs to understand the key patterns essential to attendees during anime conventions in the USA and applied SOR theory to its investigation.

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.

The purpose of this study was to examine the preservice educational narratives of Black English teachers in an effort to determine their experiences within teacher education programs with assigned white cooperating teachers.

Drawing upon Black storytelling, testimony and breath in narrative analysis, this study showcases how Black preservice teachers navigated regularized surveillance and abandonment as part of student teaching practicum.

The authors argue that, in response to their treatment, these Black preservice teachers created resistance strategies as a way to fill the mentorship void and sustain their own future teaching careers.

The literature on Black preservice teachers does the critical work of examining how they experience their racial, linguistic and gendered identities in the classroom; however, this study focuses on their experiences with white cooperating teachers – an underresearched area in the past 10 years.

Traditional construction materials and methodologies are often perceived to be unproductive, labour-intensive and detrimental to the environment. Mass-engineered timber (MET) is a new structural material that is capable of overcoming numerous issues that otherwise affect the built environment. This study was formulated to assess the current attitude and perception of young Singaporeans towards the concept of Engineered Timber Residential Buildings (ETRBs).

The study employs the mixed-method approach. Questionnaires were used as the primary mode of data gathering. These were disseminated to Singaporeans between the age of 18 and 35 years. A total of 179 valid responses were gathered. Semi-structured interviews were subsequently conducted with six individuals with different demographics in order to gain further insightful opinions and to allow cross validation of responses.

Statistical analysis revealed that 80% of respondents were willing to accept ETRBs, but a lack of awareness and knowledge of MET and the presence of misconceptions, such as an association with deforestation, may present concerns. The study also revealed that individual acceptance of ETRBs is not affected by demographics.

The production of MET involves lower overall carbon emissions than that of conventional materials, and this also allows adoption of the Design for Manufacturing and Assembly (DfMA) concept and offers the benefit of carbon sequestration. Residential buildings are the second most common building type in Singapore; significant benefits can be gained if MET is used as the primary material for residential buildings. In general, young stakeholders in Singapore welcome the concept of ETRBs, despite possessing uncertainties about ETRBs—understandable given that the material lacks a track record of usage. Public authorities are thus advised to explore the feasibility of materialising the concept of ETRBs as an option for public housing.

Smart community construction (SCC) and efficiency require resident participation. This paper aims to explore the determinants of residents’ participation intention (RPI) in the SCC.

Based on the theory of planned behavior (TPB), this study proposed an extended conceptual model to deeply analyze the RPI in the SCC. The relationship between all constructs was verified by processing and analyzing online survey data using confirmatory factor analysis (CFA), structural equation model (SEM), and bootstrapping method.

Participation attitude, perceived behavioral control, subjective norm, and perceived usefulness significantly and positively affected the RPI. Furthermore, intermediary effects in the extended conceptual model had been confirmed.

To fill the critical gap in the research on the determinants of the RPI in the SCC context, this study developed a novel conceptual model by extending the TPB to analyze the effects of self-driven and externally-driven factors on the RPI from the perspectives of residents’ psychology and external environment. The findings not only clarify the complex process of forming the RPI in the SCC but also provide a theoretical foundation for studying the RPI in similar community construction projects. Additionally, several strategies have been proposed to encourage residents’ participation in the SCC and promote the development of smart communities, such as clarifying residents’ participation obligations, improving the convenience services of smart communities, and diversifying residents’ participation approaches.

This study aims to analyze government hotline text data and generating forecasts could enable the effective detection of public demands and help government departments explore, mitigate and resolve social problems.

In this study, social problems were determined and analyzed by using the time attributes of government hotline data. Social public events with periodicity were quantitatively analyzed via the Prophet model. The Prophet model is decided after running a comparison study with other widely applied time series models. The validation of modeling and forecast was conducted for social events such as travel and educational services, human resources and public health.

The results show that the Prophet algorithm could generate relatively the best performance. Besides, the four types of social events showed obvious trends with periodicities and holidays and have strong interpretable results.

The research could help government departments pay attention to time dependency and periodicity features of the hotline data and be aware of early warnings of social events following periodicity and holidays, enabling them to rationally allocate resources to handle upcoming social events and problems and better promoting the role of the big data structure of government hotline data sets in urban governance innovations.

This paper aims to explore the implications of algorithmic management on careers and employment relationships in the Nigerian gig economy. Specifically, drawing on labour process theory (LPT), this study provides an understanding of the production relations beyond the “traditional standard” to “nonstandard” forms of employment in a gig economy mediated by digital platforms or digital forms of work, especially on ride-hailing platforms (Uber and Bolt).

This study adopted the interpretive qualitative approach and a semi-structured interview of 49 participants, including 46 platform drivers and 3 platform managers from Uber and Bolt.

This study addresses the theoretical underpinnings of the LPT as it relates to algorithmic management and control in the digital platform economy. The study revealed that, despite the ultra-precarious working conditions and persistent uncertainty in employment relations under algorithmic management, the underlying key factors that motivate workers to engage in digital platform work include higher job flexibility and autonomy, as well as having a source of income. This study captured the human-digital interface and labour processes related to digital platform work in Nigeria. Findings of this study also revealed that algorithmic management enables a transactional exchange between platform providers and drivers, while relational exchanges occur between drivers and customers/passengers. Finally, this study highlighted the perceived impact of algorithmic management on the attitude and performance of workers.

The research presents an interesting case study to investigate the influence of algorithmic management and labour processes on employment relationships in the largest emerging economy in Africa.