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The purpose of this paper is to provide a transcript of a dialogue among literacy educators and researchers on the impact of generative aritficial intelligence (AI) in the field. In the spring of 2023, a lively conversation emerged on the National Council of Research on Language and Literacy (NCRLL)’s listserv. Stephanie initiated the conversation by sharing an op-ed she wrote for Atlanta Journal-Constitution about the rise of ChatGPT and similar generative AI platforms, moving beyond the general public’s concerns about student cheating and robot takeovers. NCRLL then convened a webinar of eight leading scholars in writing and literacies development, inspired by that listerv conversation and an organizational interest in promoting intergenerational collaboration among literacy scholars.

As former doctoral students of two of the panel participants, webinar facilitators Grace and Victoria positioned themselves primarily as learners about this topic and gathered questions from colleagues, P-16 practitioners and those outside the field of education to assess the concerns and wonderings that ChatGPT and generative AI have raised. The following webinar conversation was recorded on two different days due to scheduling conflicts. It has been merged and edited into one dialogue for coherence and convergence.

Panel participants raise a host of questions and issues that go beyond topics of ethics, morality and basic writing instruction. Furthermore, in dialogue with one another, they describe possibilities for meaningful pedagogy and critical literacy to ensure that generative AI is used for a socially just future for students. While the discussion addressed matters of pedagogy, definitions of literacy and the purpose of (literacy) education, other themes included a critique of capitalism; an interrogation of the systems of power and oppression involved in using generative AI; and the philosophical, ontological, ethical and practical life questions about being human.

This paper provides a glimpse into one of the first panel conversations about ChatGPT and generative AI in the field of literacy. Not only are the panel members respected scholars in the field, they are also former doctoral students and advisors of one another, thus positioning all involved as both learners and teachers of this new technology.

Performance framework (PF) is a well-established practice to measure innovation performance and identify improvement opportunities. However, whether PFs academic research are applicable to companies remains unclear, as well as their support in the definition of improvement actions. This study aims to present the implementation and assessment of a new and updated PF proposed in previous research in a real industrial context.

The PF was implemented through an in-depth case study carried out in a European machinery manufacturer and further assessed by practitioners.

The results indicate that the PF enabled the creation of a multidimensional view of the innovation performance and the definition of improvement projects in the company. Additionally, the findings also reveal an overall positive assessment of the PF by senior managers who work with the innovation process.

As a case study, this research is inherently limited in the extent to which results can be generalised. Thus, the analyses are reductive and rationalising. Future research is needed to assess the replicability of the PF.

The study's practical contribution is based on the combination of insights and steps that provide a straightforward and actionable approach for the company to improve performance.

This study aims to advance the importance of implementing the new and updated PF after its proposition, which is often overlooked in preceding research. Furthermore, the assessment of the PF also enables to infer its value to the company's employees.

Although humanoid robots are increasingly adopted in many business settings, the dynamic effects of anthropomorphism and the functional perceptions of service robots on consumers’ responses remain unclear. This paper aims to examine the impacts of robot anthropomorphism on consumers’ trust, receptivity and the downstream effect on satisfaction. Furthermore, it examines the mediating effects of perceived ease of use (PEOU) and perceived usefulness (PU) in the relationship between anthropomorphism and consumer responses.

After conducting two separate pilot studies to help design the research materials, this research involves three sequential studies. In studies 1A and 1B, the authors used two distinct humanoid robots (i.e. Connie and Pepper) to test the direct effects of anthropomorphism on trust and receptivity and the mediated effects via PEOU and PU. Study 2 conducted a 2 (robot appearance: machine-like vs. human-like) × 2 (task complexity: low vs. high) between-subjects experimental design to further explore the boundary effects of task complexity on trust and customer satisfaction.

This research theorizes and empirically examines the mediating effects of PEOU and PU in the relationship between anthropomorphism and consumers’ responses (i.e. trust and receptivity) to service robots. Results also demonstrate a moderating role of task complexity, whereby only when the task was complex did anthropomorphism affect consumer responses and customer satisfaction. The parallel mediations of PEOU and PU were also confirmed. However, when task complexity was low, the authors observed no differences between human- and machine-like robots.

First, this research used a scenario-based method by exposing participants to different pictures or videos of service robots and measuring individuals’ responses. Consumers may respond differently upon interacting with robots in actual service contexts. Second, future research could investigate the effects of other aspects of anthropomorphism, such as robots’ voice characteristics (gender, high/low pitch), verbal communication styles and emotional expression. Finally, future research could explore other service contexts to test the generalizability of the findings.

Findings of this study also provide useful insight for companies interested in adopting service robots. First, the authors unearthed several positive outcomes of using human-like versus machine-like robots in service settings. Despite concerns about the perceived creepiness and discomfort associated with human-like robots, managers should not worry about these service agents’ potential negative effects. Second, it shows that human-like robots’ competitive advantage over machine-like robots stands out when task complexity is high. Managers should therefore carefully consider relevant service characteristics and task requirements when deciding whether to adopt robots.

This study provides original and valuable contributions to the growing literature on service robots by addressing scholarly incongruencies regarding the impact of anthropomorphism and disentangling its positive influence on consumers’ perceptions and acceptance of service robots. This study also contributes to research on technology acceptance and service robot receptivity by empirically demonstrating the mediating role of PEOU and PU. Furthermore, this research enriches the body of knowledge on task-technology fit by providing evidence that task complexity is a crucial factor to consider in service robot design.

This study aimed to understand better what makes esports fans engage with streamers' live-streaming of esports gameplay. This study used the Theory of Planned Behavior (TPB) and additionally adopted streamer identification and esports game identification as moderating variables.

Data were collected from streamers' esports content streaming viewers over 18 years of age using an online survey in Amazon M-Turk (N = 307). Based on past esports live-streaming weekly watching hours, which range from 1 to 45 h, the participants were divided into lower (n = 152) and higher (n = 155) frequency groups. PLS-SEM and bootstrapping techniques were used to test the moderated mediation relationships among the constructs.

This study found a negative moderating effect of past watching experience on the relationship between attitudes and behavioral intention, and it positively moderated the path between perceived behavioral control and behavioral intention. Also, it was found statistically significant direct impacts of streamer identification (STI) and esports game identification (EGI) on attitude and subjective norms. While the indirect impact of STI on behavioral intention through attitude was statistically significant, there were no significant indirect impacts of EGI on attitude and behavioral intention through subjective norms.

Theoretically, this study extends the TPB model by exploring the two identifications (i.e. streamers and esports games) as antecedents of the focal TPB factors (i.e. attitudes, subjective norms and perceived behavioral control) and the moderating effect of prior experience based on high/low weekly watching frequencies. Practically, content creators of esports live-streaming and live-streaming platform managers can use the study’s findings to develop strategies to nurture their current and future viewership.

Granting ‘sanctuary’ in the United States to those fleeing poverty and violence is rooted in a deep history of hospitality and compassion. As we are witnessing a rise in xenophobia accompanied by policies of exclusion, we also see a rising number of ‘sanctuary jurisdictions’ limiting their cooperation with immigration authorities that many communities consider are using extremely harsh and punitive measures to detain and deport irregular migrants. As this chapter will demonstrate, there has been a dramatic increase through ‘immigration federalism’ of the number of these jurisdictions in cities, states and municipalities since the first practices of ‘sanctuary’ in the 1980s as result of the waves of forced migration due to the civil wars in Central America. The author also proposes that those entities granting ‘sanctuary’ to irregular migrants should also apply practices of ‘compassionate migration’ as described in the chapter to expand their protections further and include them in their communities.

The overall aim of this chapter is to focus on the process of, and issues warranting consideration for, the evaluation of educational interventions. In particular, to outline some key considerations for educators to follow when assessing the evidence-base for interventions they might be considering for use in their practice. Also, important considerations for those wishing to evaluate the effectiveness of an intervention they have initiated, as well as a useful checklist which summarises this all. Recognising that some readers of this chapter might be practitioners rather than researchers, it has been written with the practitioner in mind in, hopefully, a simple and practical way. There are, however, further opportunities for additional reading and resources signposted throughout for those who wish to read up on any of these areas more. In addition to those cited throughout and referenced in the Reference List at the end. There is also a section that provides the author’s Additional Recommended Readings and Resources to follow-up on. Readers might also want to refer to Chapter 3 in this book which discusses Single versus Multiple PPI approaches.

With gaining popularity, online communities are increasing. It is leading to the data and information overflow. So, there are some challenges like cyber frauds, cyberbullying, etc. while engaging with online communities. Not only this, anonymity of the participants, stress and racism are also big challenges in online communities' interaction. Online harassers' attack tactics have changed over time. In addition, there are challenges like quality of discussion, inequality in participation of the users, etc. may scale online communities towards incitement and activism. Therefore, this study will try to analyse these challenges for overall benefit of the society.

The underlying fuzzy set theory is employed to handle the fuzziness of users' perceptions since the attributes are expressed in linguistic preferences. Through exhaustive literature review, the authors have identified 15 challenges. These challenges are further categorised as cause and effect by using DEMATEL (Decision-Making Trial and Evaluation Laboratory) approach.

Lack of strategic planning and uninspired discussions between users has emerged as a major challenge in cause category. This study further demonstrates how individual challenge can be managed and developed to navigate the online communities to maintain a healthy environment in society.

Results are based on limited dataset. Therefore, findings cannot be generalised for all online communities.

The research findings offer a suitable direction to policymakers to formulate and design policies, laws and regulations to increase user engagement in the online community. The study is beneficial to firms and researchers in understanding the factors influencing effective management of online communities.

This study explores match related factors and their impact on the batting strike rate in Twenty20 cricket – an aspect which can generate excitement and fan engagement in cricket matches.

Data was collected from www.cricinfo.com using a web scraping tool based on R programming from February 17, 2005, to October 25, 2022, numbering 4,221 men’s Twenty20 international innings featuring 41 national teams that had taken place in 85 venues across 11 countries of play. Hypothesis testing was conducted using one-way ANOVA.

The findings indicate that batters score faster in the first inning of a match, and mean strike rates also vary significantly based on the country of play. Further, the study analyses the top performing national sides, venues and country of play in terms of mean batting strike rate, thus providing insights to cricket boards, international regulating bodies of cricket, sponsors, media companies and coaching staff for better decision-making based on batting strike rate.

The originality of the study lies in its focus on using non-marketing strategies to increase fan engagement. Further, this study is the first one to examine different venues from the perspective of batting strike rate in men’s Twenty20 international matches.

The inclusion of LGBTQ + persons (lesbian, gay, bisexual, transgender, queer and having other sexual orientations and gender identities) is a crucial step in improving gender diversity in the workplace; however, till date, it remains a significant challenge for human resource management professionals. The current study critically examines this issue of an inclusive workplace for LGBTQ + people through a systematic review of the existing research that has empirically studied their experiences at the workplace. It also examines the resistance and challenges organizations face in LGBTQ + diversity training and provides future research avenues.

For systematically reviewing the literature, Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) model has been used. A total of 101 empirical studies have been reviewed.

The result shows that LGBTQ + people encounter multiple negative workplace experiences, including proximal (hiring discrimination and housing discrimination) and distal workplace discrimination (unsafe work climate, microaggressions and harassment). These aversive experiences lead to work stress while also mandating that people manage their sexual identity and style of dressing. This stress, in turn, impacts their work–family outcomes, job satisfaction and decision-making with regard to their careers.

The paper provides a holistic understanding of the aversive workplace experiences encountered by sexual minorities.

Research on artificial intelligence (AI) and its potential effects on the workplace is increasing. How AI and the futures of work are framed in traditional media has been examined in prior studies, but current research has not gone far enough in examining how AI is framed on social media. This paper aims to fill this gap by examining how people frame the futures of work and intelligent machines when they post on social media.

We investigate public interpretations, assumptions and expectations, referring to framing expressed in social media conversations. We also coded the emotions and attitudes expressed in the text data. A corpus consisting of 998 unique Reddit post titles and their corresponding 16,611 comments was analyzed using computer-aided textual analysis comprising a BERTopic model and two BERT text classification models, one for emotion and the other for sentiment analysis, supported by human judgment.

Different interpretations, assumptions and expectations were found in the conversations. Three subframes were analyzed in detail under the overarching frame of the New World of Work: (1) general impacts of intelligent machines on society, (2) undertaking of tasks (augmentation and substitution) and (3) loss of jobs. The general attitude observed in conversations was slightly positive, and the most common emotion category was curiosity.

Findings from this research can uncover public needs and expectations regarding the future of work with intelligent machines. The findings may also help shape research directions about futures of work. Furthermore, firms, organizations or industries may employ framing methods to analyze customers’ or workers’ responses or even influence the responses. Another contribution of this work is the application of framing theory to interpreting how people conceptualize the future of work with intelligent machines.