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This study aims to measure the perception of the residents regarding peer assisted learning in emergency medicine residency program as well as assessing the psychometric properties of the questionnaire used as the research tool.

A descriptive cross-sectional study was conducted at King Abdullah Medical City, Saudi Arabia, Makkah on a Convenience sample of Emergency Medicine residents who completed peer assisted learning sessions. Data were collected through a questionnaire evaluating the residents’ perception of Peer Assisted Learning. Descriptive statistics were used and statistical significance was set at p < 0.05. The Peer Assisted Learning (PAL) satisfaction questionnaire was tested for construct validity and reliability. Exploratory Factor Analysis (EFA) was conducted. The reliability of the used questionnaire was assessed using Cronbach’s coefficient alpha.

The study showed that the PAL questionnaire yielded four factors. The four factors are labeled as the following: Factor 1: Cognitive congruence, Factor 2: Social congruence; Factor 3: Teaching skills in Peer Assisted Learning (PAL) session; and Factor 4: Professional development. These results indicated that questionnaire has a good structure validity. In addition, the study showed high internal consistency of the questionnaire. Cronbach’s alpha coefficient value for the total scale was 0.88. The perception of residents was assessed and results revealed residents’ satisfaction regarding Peer Assisted Learning.

Up to the authors’ knowledge, this is the first study which assessed the psychometric properties of a questionnaire measuring the residents’ perceptions regarding Peer Assisted Learning. The study contributed to improving information regarding the psychometric properties and usefulness of PAL questionnaire.

Following the increasing trend of artificial intelligence (AI) research in hospitality literature, this critical reflection paper aims to identify AI-assisted mindfulness as a critical yet under-investigated issue and to contribute feasible directions for future research.

The authors first conceptualize a framework explaining the effects of mindfulness design in AI interventions on improving human mindfulness. The authors then identify opportunities for interventions in AI-assisted mindfulness for the tourism, hospitality and events industries. Finally, the authors propose potential themes for AI-assisted mindfulness research.

This study contributes three major conceptual works. First, we conceptualize a framework of AI-assisted mindfulness, showcasing that the scope of AI-assisted mindfulness spans from AI interventions to state mindfulness and then to trait mindfulness. Second, the authors offer two approaches to strategic thinking, one from mindfulness (i.e. mindfulness-focused niche markets and activities) and one from AI applications (i.e. AI-facilitated devices and platforms), to identify opportunities for AI-assisted mindfulness interventions. Third, for both management- and marketing-oriented AI-assisted mindfulness research, the authors propose 18 themes.

This critical reflection paper offers directions for future knowledge creation in AI-assisted mindfulness in the tourism, hospitality and events industries.

To the best of the authors’ knowledge, this critical reflection paper serves as the first in hospitality and tourism literature to systematically propose the research issue of AI-assisted mindfulness, offering directions and themes for future research.

Asynchronous Video Interviews (AVIs) incorporating Artificial Intelligence (AI)-assisted assessment has become popular as a pre-employment screening method. The extent to which applicants engage in deceptive impression management (IM) behaviors during these interviews remains uncertain. Furthermore, the accuracy of human detection in identifying such deceptive IM behaviors is limited. This study seeks to explore differences in deceptive IM behaviors by applicants across video interview modes (AVIs vs Synchronous Video Interviews (SVIs)) and the use of AI-assisted assessment (AI vs non-AI). The study also investigates if video interview modes affect human interviewers' ability to detect deceptive IM behaviors.

The authors conducted a field study with four conditions based on two critical factors: the synchrony of video interviews (AVI vs SVI) and the presence of AI-assisted assessment (AI vs Non-AI): Non-AI-assisted AVIs, AI-assisted AVIs, Non-AI-assisted SVIs and AI-assisted SVIs. The study involved 144 pairs of interviewees and interviewers/assessors. To assess applicants' deceptive IM behaviors, the authors employed a combination of interviewee self-reports and interviewer perceptions.

The results indicate that AVIs elicited fewer instances of deceptive IM behaviors across all dimensions when compared to SVIs. Furthermore, using AI-assisted assessment in both video interview modes resulted in less extensive image creation than non-AI settings. However, the study revealed that human interviewers had difficulties detecting deceptive IM behaviors regardless of the mode used, except for extensive faking in AVIs.

The study is the first to address the call for research on the impact of video interview modes and AI on interviewee faking and interviewer accuracy. This research enhances the authors’ understanding of the practical implications associated with the use of different video interview modes and AI algorithms in the pre-employment screening process. The study contributes to the existing literature by refining the theoretical model of faking likelihood in employment interviews according to media richness theory and the model of volitional rating behavior based on expectancy theory in the context of AVIs and AI-assisted assessment.

This study investigates whether the visualization of agricultural products influences consumers’ purchase intentions in the context of farmer-assisted livestreaming in China. Moreover, it explores the moderating effect of packaging functionality and the mediating effect of consumer trust.

Consumers in China from multiple social media platforms participated in this survey, which yielded 333 valid responses for analysis.

The results revealed a positive relationship between the video presentation about the agricultural production process and consumers’ purchase intention, which is mediated by consumers’ trust. Meanwhile, packaging functionality moderates the relationship between agricultural product visualization and consumers’ purchase intentions as well as the indirect effect of consumers’ trust.

This study extends the application of the stimulus-organism-response (SOR) model to the field of farmer-assisted livestreaming. By building a more detailed model, this study adds to knowledge on the influencing mechanisms of consumers’ purchase intentions in farmer-assisted livestreaming.

The purpose of this paper is to demonstrate that the eventual legalisation of assisted dying in England and Wales should extend to persons deprived of liberty, as well.

Using a relational view of dignity strengthens the argument that the need to protect the dignity of persons deprived of liberty requires the extension of the legalisation of assisted dying to persons deprived of liberty once generally achieved in England and Wales.

Three aspects make dignity a relational concept – dignity being attributed by society, dignity working as a restraint on others and dignity requiring specific behaviours. All these elements support the claim that assisted dying should be available to persons deprived of liberty, once legalised in England and Wales.

Both the legalisation of assisted dying for persons deprived of liberty and the concept of relational dignity have to date found little attention. With a legalisation of assisted dying being debated in England and Wales, it is pertinent to discuss its application to persons deprived of liberty, who would undoubtedly not automatically benefit from a future Assisted Dying Act.

Voice assistant technology represents one of the most radical artificial intelligence innovations. Drawing on the processing fluency theory and consumer learning literature, this study aims to explore how consumer acceptance of new products is influenced by voice assistant function (VAF), along with the impacts of role clarity and learning modality.

Four between-subjects experimental studies were conducted. Study 1 tested the main effect of VAF on consumer acceptance. Study 2 included role clarity as a mediator between VAF and consumer acceptance. Study 3 examined the moderation effect of learning modality and contrasted the effectiveness of experiential and verbal learning in helping increase consumer acceptance. Study 4, as a post hoc study, tested serial mediations to validate whether processing fluency was indeed the mechanism explaining the indirect relationship between VAF and consumer acceptance via role clarity.

The negative impact of VAF on consumer acceptance was demonstrated in all four studies. Studies 2 and 3 showed VAF decreased role clarity which further influenced consumer acceptance. Moreover, Study 3 evidenced that experiential learning was more effective than verbal learning in increasing consumer acceptance of voice-assisted products via role clarity. Study 4 demonstrated that VAF decreased role clarity, which in turn decreased processing fluency, leading to lower consumer acceptance.

This research views the usage of voice-assisted products as a coproduction process between consumers and the VAF. Accordingly, findings provide novel insights into processing fluency of tasks assisted by VAF through the lens of role clarity and learning modality, which enriches the understanding of potential barriers and opportunities for consumers to accept voice-assisted products.

The purpose of this study is to examine relevant demographic and socio-economic factors as they relate to progress towards intercultural communicative competence (ICC) and intercultural sensitivity for ethnic-minority Latinx middle school English learners (ELs) using a mobile-assisted funds-of-knowledge-featured writing practice.

Through the theoretical lens of funds of knowledge, this three-year study implemented a survey-based quasi-experimental design centered on the Latinx ELs’ ICC development with the implementation of an intercultural sensitivity questionnaire (Chen and Starosta, 2000). The authors first investigated the relationship between ELs’ intercultural sensitivity and associated demographic and socio-economic factors. The authors then examined the changes of ELs’ intercultural sensitivity. Over ten weeks, the intervention group completed five funds-of-knowledge-featured narrative essays using pen and paper and mobile-based writing tools alternatively.

Findings indicated that ELs’ intercultural sensitivity increased as they advanced to a higher-level grade from sixth to eighth. The embedded mobile-assisted funds-of-knowledge writing practice as intervention promoted ELs’ intercultural sensitivity in interaction engagement, respect of cultural differences, interaction enjoyment and interaction attentiveness. Among the variables, interaction enjoyment was portrayed the most. ELs who reported not speaking English at home were statistically significant in this experiment.

This study acknowledges the robust and variance of funds of knowledge as a niche to address the interculturality and hybridity of ELs’ cultural practices accumulated through Latinx ELs’ family socialization and social development using mobile-assisted writing practice. This study could provide implications for optimizing inclusive experience to promote computer-assisted language learning in a contemporary, postcolonial global world.

In this article, the outcomes of a survey aimed to investigate how aware of and how capable coaches in higher vocational Dutch education perceive themselves to assist students displaying mental health and well-being issues are presented. Additionally, the article explores coaches’ perceptions regarding the frequency, form of help offered, topics to be tackled and the preferred form in which this help should be provided.

The author conducted a survey that gathered qualitative and quantitative data from coaches (N = 82) at a Dutch University of Applied Sciences in the north of the Netherlands. A differentiation in coaches’ number of years of teaching and coaching experience was considered.

The outcomes of the data analyses showed that overall, coaches claimed to be very aware of students’ mental health and well-being-related issues and that female coaches tend to be more aware of these than male coaches. The group of coaches with 5–25 years of coaching experience resulted in being less trained to notice when students struggle with mental health and well-being issues. Overall, coaches indicated to be tentatively willing to assist such students and reported to have a rather low ability and capability to assist students who displayed mental health and well-being issues. More than half of the respondents declared that “face to-face” was the most appropriate approach to address mental health and well-being topics, and most of the respondents (43%) answered that it should be “offered at student’s request.” Some suggested topics to be offered were stress, depression, anxiety, study-related issues, study motivation, persistence, emotional intelligence and emotional resilience. Coaches proposed to be provided with trainings that equip them with the necessary knowledge, tools, and concrete mental health and well-being topics that could be addressed during coaching. Additionally, there should be a clear distinction between professional mental health help and coaching for mental health and well-being in universities.

There were very few studies that reported on coaching for mental health and well-being in higher education after the Covid-19 pandemic in the Netherlands to compare the results with; the sample size of this survey was small; the survey was designed to capture only the coaches’ perceptions on students’ mental health-related issues.

By performing this survey, more empirical knowledge is added regarding higher education coaches’ perception of their awareness, willingness, capability and ability to assist students who display mental health and well-being issues in general, and students affected by the impact of the Covid-19 pandemic in particular. Furthermore, insights regarding higher education coaches’ perception on the frequency, form of the help offered, topics to be tackled and form in which this help to be offered were gathered.

By performing this survey, more empirical knowledge is added regarding higher education coaches’ perception of their awareness, willingness, capability and ability to assist students who display mental health and well-being issues in general, and students affected by the impact of the Covid-19 pandemic in particular. Furthermore, insights regarding higher education coaches’ perception of the frequency, form of the help offered, topics to be tackled and the preferred form in which this help should be offered were gathered.

The purpose of this study is to comprehend how AI aids marketers in engaging customers and generating value for the company by way of customer engagement (CE). CE is a popular area of research for scholars and practitioners. One area of research that could have far-reaching ramifications with regard to strengthening CE is artificial intelligence (AI). Consequently, it becomes extremely important to understand how AI is helping the marketer reach customers and create value for the firm via CE.

A detailed approach using both systematic review and bibliometric analysis was used. It involved identifying key research areas, the most influential authors, studies, journals, countries and organisations. Then, a comprehensive analysis of 50 papers was carried out in the four identified clusters through co-citation analysis. Furthermore, a content analysis of 42 articles for the past six years was also conducted.

Emerging themes explored through cluster analysis are CE concepts and value creation, social media strategies, big data innovation and significance of AI in tertiary industry. Identified themes for content analysis are CE conceptualisation, CE behaviour in social media, CE role in value co-creation and CE via AI.

CE has emerged as a topic of great interest for marketers in recent years. With the rapid growth of digital media and the spread of social media, firms are now embarking on new online strategies to promote CE (Javornik and Mandelli, 2012). In this review, the authors have thoroughly assessed multiple facets of prior research papers focused on the utilisation of AI in the context of CE. The existing research papers highlighted that AI-powered chatbots and virtual assistants offer real-time interaction capabilities, swiftly addressing inquiries, delivering assistance and navigating customers through their experiences (Cheng and Jiang, 2022; Naqvi et al., 2023). This rapid and responsive engagement serves to enrich the customer’s overall interaction with the business. Consequently, this research can contribute to a comprehensive knowledge of how AI is assisting marketers to reach customers and create value for the firm via CE. This study also sheds light on both the attitudinal and behavioural aspects of CE on social media. While existing CE literature highlights the motivating factors driving engagement, the study underscores the significance of behavioural engagement in enhancing firm performance. It emphasises the need for researchers to understand the intricate dynamics of engagement in the context of hedonic products compared to utilitarian ones (Wongkitrungrueng and Assarut, 2020). CEs on social media assist firms in using their customers as advocates and value co-creators (Prahalad and Ramaswamy, 2004; Sawhney et al., 2005). A few of the CE themes are conceptual in nature; hence, there is an opportunity for scholarly research in CE to examine the ways in which AI-driven platforms can effectively gather customer insights. As per the prior relationship marketing studies, it is evident that building relationships reduces customer uncertainty (Barari et al., 2020). Therefore, by using data analysis, businesses can extract valuable insights into customer preferences and behaviour, equipping them to engage with customers more effectively.

The rapid growth of social media has enabled individuals to articulate their thoughts, opinions and emotions related to a brand, which creates a large amount of data for VCC. Meanwhile, AI has emerged as a radical way of providing value content to users. It expands on a broader concept of how software and algorithms work like human beings. Data collected from customer interactions are a major prerequisite for efficiently using AI for enhancing CE. AI not only reduces error rates but, at the same time, helps human beings in decision-making during complex situations. Owing to built-in algorithms that analyse large amounts of data, companies can inspect areas that require improvement in real time. Time and resources can also be saved by automating tasks contingent on customer responses and insights. AI enables the analysis of customer data to create highly personalised experiences. It can also forecast customer behaviour and trends, helping businesses anticipate needs and preferences. This enables proactive CE strategies, such as targeted offers or timely outreach. Furthermore, AI tools can analyse customer feedback and sentiment across various channels. This feedback can be used to make necessary improvements and address concerns promptly, ultimately fostering stronger customer relationships. AI can facilitate seamless engagement across multiple digital channels, ensuring that customers can interact with a brand through their preferred means, be it social media, email, or chat. Consequently, this research proposes that practitioners and companies can use analysis performed by AI-enabled systems on CEB, which can assist companies in exploring the extent to which each product influences CE. Understanding the importance of these attributes would assist companies in developing more memorable CE features.

This study examines how prominent CE and AI are in academic research on social media by identifying research gaps and future developments. This research provides an overview of CE research and will assist academicians, regulators and policymakers in identifying the important topics that require investigation.

An abundance of techniques has been presented so forth for waste classification but, they deliver inefficient results with low accuracy. Their achievement on various repositories is different and also, there is insufficiency of high-scale databases for training. The purpose of the study is to provide high security.

In this research, optimization-assisted federated learning (FL) is introduced for thermoplastic waste segregation and classification. The deep learning (DL) network trained by Archimedes Henry gas solubility optimization (AHGSO) is used for the classification of plastic and resin types. The deep quantum neural networks (DQNN) is used for first-level classification and the deep max-out network (DMN) is employed for second-level classification. This developed AHGSO is obtained by blending the features of Archimedes optimization algorithm (AOA) and Henry gas solubility optimization (HGSO). The entities included in this approach are nodes and servers. Local training is carried out depending on local data and updations to the server are performed. Then, the model is aggregated at the server. Thereafter, each node downloads the global model and the update training is executed depending on the downloaded global and the local model till it achieves the satisfied condition. Finally, local update and aggregation at the server is altered based on the average method. The Data tag suite (DATS_2022) dataset is used for multilevel thermoplastic waste segregation and classification.

By using the DQNN in first-level classification the designed optimization-assisted FL has gained an accuracy of 0.930, mean average precision (MAP) of 0.933, false positive rate (FPR) of 0.213, loss function of 0.211, mean square error (MSE) of 0.328 and root mean square error (RMSE) of 0.572. In the second level classification, by using DMN the accuracy, MAP, FPR, loss function, MSE and RMSE are 0.932, 0.935, 0.093, 0.068, 0.303 and 0.551.

The multilevel thermoplastic waste segregation and classification using the proposed model is accurate and improves the effectiveness of the classification.