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This study aims to explore the implementation of artificial intelligence (AI) into recruitment by considering its potential to maximise the effectiveness of the human resources (HR) processes, challenges associated with the implementation and ethical concerns.

A qualitative research approach was used to reach the stated objectives within the context of the small open economy – the Czech Republic. Interviews were conducted with four participants, Czech-based recruiters, each with five or more years of experience in their field. The interviews were conducted in Autumn 2023 within the online platform. The answers were transcribed and thematically analysed.

The participants who were interviewed heavily emphasised the importance of the role of the human factor in recruitment, yet several observations and insights were obtained. In particular, some interviewees indicated a possible usage of a chatbot for the first round of the candidates' selection, but they see it as problematic in the final decision on the position fulfilment, where the human factor is not replaceable so far. The key ethical challenges of the broader implementation of AI in the recruitment practices of the respondents remain the risks regarding privacy and data protection, especially the General Data Protection Regulation (GDPR) legislation.

This article delivers pertinent insights for recruiters on using AI in recruitment, bringing forth a more subtle understanding of the faceted subject of AI-based recruitment.

Digitally driven virtual streamers are increasingly utilized in live-streaming commerce, possessing distinct advantages compared to human streamers. However, the applicable scenarios of virtual streamers are still unclear. Focusing on product attribute variances, this paper compares the livestreaming effects of virtual and human streamers to clarify the applicable scenarios for each and assist companies in strategically choosing suitable streamers.

We conducted four experiments utilizing both images and video as stimulus materials, and each experiment employed different products. To test the proposed model, a total of 1,068 valid participants were recruited, encompassing a diverse group of individuals, including undergraduates and employed workers.

The results indicate no significant difference between virtual and human streamers in increasing consumers’ purchase intention for utilitarian products. In contrast, human streamers are more effective in enhancing consumer purchase intention for hedonic products, with a mediating role of mental imagery quality. Consumers’ implicit personality variances also influence their willingness to accept virtual streamers.

This paper is the first to compare the effects of virtual and human streamers in promoting different products to enhance our comprehension of virtual streamers. Given the potential risks associated with human streamers, a comprehensive understanding of the role of virtual streamers is imperative for brands when deploying live-streaming commerce activities.

The purpose of this study is to explore the process of initial sensemaking that organizational members activate when they reflect on AI adoption in their work settings, and how the perceived features of AI technologies trigger sensemaking processes which in turn have the potential to influence workplace learning modes and trajectories.

We adopted an explorative qualitative and interactive approach to capture free fantasies and imaginative ideas of AI among people within the industry. We adopt a conceptual perspective that combines theories on initial sensemaking and workplace learning as a theoretical lens to analyze data collected during 23 focus groups held at four large Swedish manufacturing companies. The data were analyzed using the Gioia method.

Two aggregated dimensions were defined and led to the development of an integrated conceptualization of the initial sensemaking of AI technology adoption. Specifically, sensemaking triggered by abstract features of AI technology mainly pointed to an exploitative learning path. Sensemaking triggered by concrete features of the technology mainly pointed to explorative paths, where socio-technical processes appear to be crucial in the process of AI adoption.

This is one of the first studies that attempts to explore and conceptualize how organizations make sense of prospective workplace learning in the context of AI adoption.

The purpose of this study was to investigate the impact of using a jigsaw learning strategy integrated with computer simulation (JLSICS) on the academic achievement and attitudes of students, along with exploring the relationships between them in the process of learning about acids and bases.

The research design used in the study was quasi-experimental, using non-equivalent comparison groups for both pre- and post-tests. A quantitative approach was used to address the research problem, with three groups involved: two experimental and one comparative group. The treatment group, which received the JLSICS intervention, consisted of two intact classes, while the comparison group included one intact class. Data collection involved achievement tests and attitude scale tests on acid and base. Various statistical analyses such as one-way analysis of variance, one-way multivariate analysis of variance, Pearson product-moment correlation, mean and standard deviation were used for data analysis.

The study’s results revealed that the incorporation of the JLSICS had a beneficial influence on the academic achievement and attitudes of grade 10 chemistry students towards acid and base topics. The JLSICS approach proved to be more successful than both conventional methods and the standalone use of the jigsaw learning strategy (JLS) in terms of both achievement and attitudes. The research demonstrated a correlation between positive attitudes towards chemistry among high school students and enhanced achievement in the subject.

The study only focused on one specific aspect of chemistry (acid and base chemistry), which restricts the applicability of the findings to other chemistry topics or subjects. In addition, the study used a quasi-experimental design with a pretest-posttest comparison group, which may introduce variables that could confound the results and restrict causal inferences.

This study addresses the gap in instructional interventions and provides theoretical and practical insights. It emphasizes the importance of incorporating contemporary instructional methods for policymakers, benefiting the government, society and students. By enhancing student achievement, attitudes and critical thinking skills, this approach empowers students to take charge of their learning, fostering deep understanding and analysis. Furthermore, JLSICS aids in grasping abstract chemistry concepts and has the potential to reduce costs associated with purchasing chemicals for schools. This research opens doors for similar studies in different educational settings, offering valuable insights for educators and policymakers.

The originality and value of this study are in its exploration of integrating the jigsaw learning strategy with computer simulations as an instructional approach in chemistry education. This research contributes to the existing literature by showing the effectiveness of JLSICS in improving students’ achievements and attitudes towards acid and base topics. It also emphasizes the importance of fostering positive attitudes towards chemistry to enhance students’ overall achievement in the subject.

This study examines the antecedents and dynamics of authoritarian leadership and extends the effects of managers' sleep quality to employee behavior.

On the basis of self-regulation theory, 513 unit day samples were analyzed using cross-level path analysis and a Monte Carlo simulation test.

Managers' sleep quality is positively related to authoritarian leadership and positive emotions play a mediating role. Authoritarian leadership is positively related to employees' counterproductive behavior. Managers' sleep quality affects employees' counterproductive behavior through managers' positive emotions and authoritarian leadership.

Individuals should learn to reduce stress and maintain a positive mood. Organizations should reduce employees' overtime work and work stress and find other ways to improve employees' sleep quality.

First, we considered authoritarian leadership to be dynamic and studied it on a daily basis. Second, we studied the antecedents of authoritarian leadership from the perspective of leaders' states (sleep quality and emotions). Third, we discussed the effect of managers' sleep quality on employee behavior.

Postulating that individuals exposed to the threat of contagious diseases respond oversensitively toward other people, the current research aims to investigate its impact on consumers’ preferences for human images—human presence—in product packaging.

Five independent online and offline experiments were conducted. Studies 1, 2a, and 2b employed a three-group (threat: contagious vs. control vs. noncontagious) between-subjects design to investigate the main effect and its underlying mechanism. To further examine the moderation effects, Study 3 used a 2 (threat: contagious vs. control) × 2 (product feature: basic vs. antibacterial) between-subjects design, and Study 4 employed a 2 (threat: contagious vs. control) × 3 (human type: non–human vs. human–adult vs. human–baby) between-subjects design.

Studies 1, 2a, and 2b demonstrate that consumers facing the threat of contagious diseases tend to avoid social interaction, leading to a lower preference for products featuring human presence (vs. non-human presence). Studies 3 and 4 contribute to our hypothesized process by providing boundary conditions. Specifically, when the product incorporates an antibacterial function (Study 3) and the packaging depicts a baby (Study 4), the existing effect can be attenuated.

Despite the prevalence of experiencing epidemics and pandemics, little work has examined how threatened consumers respond to product packaging. The present research addresses this gap by exploring consumers' preferences for products featuring human presence on their packaging. Furthermore, this research contributes to the practical understanding of consumer choices by identifying product features and human types as moderating factors.

Financial institutions actively seek to leverage the capabilities of artificial intelligence (AI) across diverse operations in the field. Especially, the adoption of AI advisors has a significant impact on trading and investing in the stock market. The purpose of this paper is to test whether AI advisors are less preferred compared to human advisors for investing and whether this algorithm aversion diminishes for trading.

The four hypotheses regarding the direct and indirect relationships between variables are tested in five experiments that collect data from Prolific.

The results of the five experiments reveal that, for investing, consumers are less likely to use AI advisors in comparison to human advisors. However, this reluctance to AI advisors decreases for trading. The author identifies the perceived importance of careful decision-making for investing and trading as the psychological mechanism. Specifically, the greater emphasis on careful decision-making in investing, as compared to trading, leads to consumers’ tendency to avoid AI advisors.

This research is the first to investigate whether algorithm aversion varies based on whether one’s approach to the stock market is investing or trading. Furthermore, it contributes to the literature on carefulness by exploring the interaction between a stock market approach and the lay belief that algorithms lack the capability to deliberate carefully.

The United Nation’s treaty from the Convention on the Rights of Persons with Disabilities (CRPD) speaks to the assurance of rights and access to justice. To assure the rights addressed in the treaty, disability scholars have argued for a collaborative approach between police officers, mental health, Intellectual and Developmental Disabilities, professionals, and disability rights organisations. Internationally, we have witnessed that rights are being trampled at the intersection of race/ethnicity, gender identity, disability, and sexual orientation. Interactions with the police and the various systems are sometimes experienced as sources of trauma, racism, disrespect, pain, and abuse by individuals living with disabilities. Allyship and organising with the community, particularly with BIPOC and other ‘minoritised’ communities, is essential for policy and other systemic change. Community conversations were done to learn how Black, Indigenous, and People of Color (BIPOC) and allies experience and address policing and disability and act at these intersections. The advocacy and activism of Surviving Race: The Intersection of Injustice, Disability, and Human Rights served as the impetus for this study. Surviving Race was created to unite psychiatric survivors, BIPOC impacted by the mental health and disability systems, White allies, and members of the LGBQTIA+ community to stand in solidarity with activists who were demanding systemic change after the deaths of far too many. This chapter explores intersectional and cross-disability allyship, allyship to BIPOC disability, and psychiatric survivor communities. It examines how people with disabilities and allies can more effectively work at the intersection of race, rights, equity, and justice.

Notwithstanding their potential benefits especially for individuals with low health literacy, users are still somewhat skeptical about the reliability of healthcare chatbots. The present study aims to address this challenge by investigating strategies to enhance users’ cognitive and emotional trust in healthcare chatbots. Particularly, this study aims to understand the effects of chatbot design cues in increasing trust and future chatbot use intention for low health literacy users.

We conducted two experimental studies with a final sample of 327 (Study 1) and 241 (Study 2). Three different chatbots were developed (Chatbot design: Bot vs Male-doctor vs Female-doctor). Participants were asked to have a medical consultation with the chatbot. Participants self-reported their health literacy scores. The PROCESS model 7 was used to analyze the hypotheses.

The results showed that the female-doctor cues elicited greater cognitive and emotional trust, whereas the male-doctor cues only led to greater cognitive trust (vs bot-like cues). Importantly, this study found that users’ health literacy is a significant moderating factor in shaping cognitive and emotional trust. The results indicated that both the female and male-doctor cues’ positive effects on cognitive trust were significant for those with lower levels of health literacy. Furthermore, the positive effect of the female-doctor cues on emotional trust was also significant only for those whose health literacy level was low. The increased cognitive and emotional trust led to greater future intention to use the chatbot, confirming significant moderated mediation effects.

Despite the strong economic and educational benefits of healthcare chatbots for low health literacy users, studies examining how healthcare chatbot design cues affect low health literate users surprisingly remained scarce. The results of this study suggest that healthcare chatbots can be a promising technological intervention to narrow the health literacy gap when aligned with appropriate design cues.

The objective of this research is to investigate the capabilities of the ChatGPT GPT-4 model, a form of artificial intelligence (AI), in comparison to human experts in the context of construction project risk management.

Employing a mixed-methods approach, the study draws a qualitative and quantitative comparison between 16 human risk management experts from Finnish construction companies and the ChatGPT AI model utilizing anonymous peer reviews. It focuses primarily on the areas of risk identification, analysis, and control.

ChatGPT has demonstrated a superior ability to generate comprehensive risk management plans, with its quantitative scores significantly surpassing the human average. Nonetheless, the AI model's strategies are found to lack practicality and specificity, areas where human expertise excels.

This study marks a significant advancement in construction project risk management research by conducting a pioneering blind-review study that assesses the capabilities of the advanced AI model, GPT-4, against those of human experts. Emphasizing the evolution from earlier GPT models, this research not only underscores the innovative application of ChatGPT-4 but also the critical role of anonymized peer evaluations in enhancing the objectivity of findings. It illuminates the synergistic potential of AI and human expertise, advocating for a collaborative model where AI serves as an augmentative tool, thereby optimizing human performance in identifying and managing risks.