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This paper aims to meta-analyze the results of the prior studies related to the relationship of human capital and financial performance in Islamic banking.

To examine the relationship between human capital and financial of Islamic banks, 23 empirical studies having sample of 15,607 are considered for the meta-analysis. Moreover, different measures related to financial performance including return on assets (ROA), return of equity (ROE) and Tobin’s Q have been taken as moderating for further subgroup analysis.

The results of meta-analysis reveal a positive correlation between human capital and financial performance with an effect size of 0.268. The subgroup analyses showed significant positive associations of human capital with ROA and ROE, insignificant with Tobin’s Q.

This study suggests Islamic banking should prioritize human capital development, maintain consistency and adopt a long-term perspective. Future research should consider context-specific factors and harmonize human capital and financial performance measurements for consensus.

This paper reviews the pros and cons of different parametric modeling methods, which can provide a theoretical reference for parametric reconstruction of 3D human body models for virtual fitting.

In this study, we briefly analyze the mainstream datasets of models of the human body used in the area to provide a foundation for parametric methods of such reconstruction. We then analyze and compare parametric methods of reconstruction based on their use of the following forms of input data: point cloud data, image contours, sizes of features and points representing the joints. Finally, we summarize the advantages and problems of each method as well as the current challenges to the use of parametric modeling in virtual fitting and the opportunities provided by it.

Considering the aspects of integrity and accurate of representations of the shape and posture of the body, and the efficiency of the calculation of the requisite parameters, the reconstruction method of human body by integrating orthogonal image contour morphological features, multifeature size constraints and joint point positioning can better represent human body shape, posture and personalized feature size and has higher research value.

This article obtains a research thinking for reconstructing a 3D model for virtual fitting that is based on three kinds of data, which is helpful for establishing personalized and high-precision human body models.

The purpose of this study was to investigate trust within human-AI teams. Trust is an essential mechanism for team success and effective human-AI collaboration.

In an online experiment, the authors investigated whether trust perceptions and behaviours are different when introducing a new AI teammate than when introducing a new human teammate. A between-subjects design was used. A total of 127 subjects were presented with a hypothetical team scenario and randomly assigned to one of two conditions: new AI or new human teammate.

As expected, perceived trustworthiness of the new team member and affective interpersonal trust were lower for an AI teammate than for a human teammate. No differences were found in cognitive interpersonal trust and trust behaviours. The findings suggest that humans can rationally trust an AI teammate when its competence and reliability are presumed, but the emotional aspect seems to be more difficult to develop.

This study contributes to human–AI teamwork research by connecting trust research in human-only teams with trust insights in human–AI collaborations through an integration of the existing literature on teamwork and on trust in intelligent technologies with the first empirical findings on trust towards AI teammates.

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.

This paper aims to provide insight into the evolving relationship between humans and machines, understanding its multifaceted impact on our lifestyle and landscape in the past as well as in the present, with implications for the near future. It uses bibliometric analysis combined with a systematic literature review to identify themes, trace historical developments and offer a direction for future human–machine interactions (HMIs).

To provide thorough coverage of publications from the previous four decades, the first section presents a text-based cluster bibliometric analysis based on 305 articles from 2,293 initial papers in the Scopus and Web of Science databases produced between 1984 and 2022. The authors used VOS viewer software to identify the most prominent themes through cluster identification. This paper presents a systematic literature review of 63 qualified papers using the PRISMA framework.

Next, the systematic literature review and bibliometric analysis revealed four major historical themes and future directions. The results highlight four major research themes for the future: from Taylorism to advanced technologies; machine learning and innovation; Industry 4.0, Society 5.0 and cyber–physical system; and psychology and emotions.

There is growing anxiety among humankind that in the future, machines will overtake humans to replace them in various roles. The current study investigates the evolution of HMIs from their historical roots to Society 5.0, which is understood to be a human-centred society. It balances economic advancement with the resolution of social problems through a system that radically integrates cyberspace and physical space. This paper contributes to research and current limited knowledge by identifying relevant themes and offering scope for future research directions. A close look at the analysis posits that humans and machines complement each other in various roles. Machines reduce the mechanical work of human beings, bringing the elements of humanism and compassion to mechanical tasks. However, in the future, smart innovations may yield machines with unmatched dexterity and capability unthinkable today.

This paper attempts to explore the ambiguous and dynamic relationships between humans and machines. The present study combines systematic review and bibliometric analysis to identify prominent trends and themes. This provides a more robust and systematic encapsulation of this evolution and interaction, from Taylorism to Society 5.0. The principles of Taylorism are extended and redefined in the context of HMIs, especially advanced technologies.

Artificial intelligence (AI) has become increasingly important and influential in reading and writing. The influx of social media digital spaces, like TikTok, has also shifted the ways multimodal composition takes place alongside AI. This study aims to argue that within spaces like TikTok, human composers must attend to the ways they write for, with and against the AI-powered algorithm.

Data collection was drawn from a larger study on #BookTok (the TikTok subcommunity for readers) that included semi-structured interviews including watching and reflecting on a TikTok they created. The authors grounded this study in critical posthumanist literacies to analyze and open code five #BookTok content creators’ interview transcripts. Using axial coding, authors collaboratively determined three overarching and entangled themes: writing for, with and against.

Findings highlight the nuanced ways #BookTokers consider the AI algorithm in their compositional choices, namely, in the ways how they want to disseminate their videos to a larger audience or more niche-focused community. Throughout the interviews, participants revealed how the AI algorithm was situated differently as both audience member, co-author and censor.

This study is grounded in critical posthumanist literacies and explores composition as a joint accomplishment between humans and machines. The authors argued that it is necessary to expand our human-centered notions of what it means to write for an audience, to co-author and to resist censorship or gatekeeping.

This paper aims to explore the role played by the National Archives of South Africa in human rights promotion and protection. The study examined the challenges that archivists encounter when undertaking archival functions, such as acquisition, appraisal and access provision, that contribute to forming documentary archives crucial for human rights promotion and protection.

A review of literature dealing with acquisition, appraisal and access was used in this research. It was supplemented with interviews.

This paper provides recommendations of benefits in the field of archives management with a focus on the areas of acquisition, appraisal and access. The transformational discourse in the jurisdiction of archives management challenges archival institutions to be active players in selecting historical and cultural archives’ significance that is significant in human rights protections. However, despite judicial requirements that recommend the importance of archives, there is evidence that archival functions such as appraisal, acquisition and access are not being fully used, resulting in national archives institutions that are subject to irregularities that contribute to an unbalanced archives collection.

The paper was limited only to the National Archives of South Africa.

The paper makes practical implications concerning the acquisition, appraisal and providing access to human rights records.

Sufficient funding resource allocation ought to be provided to advance human rights promotion.

This paper offers informed recommendations to address the challenges of acquisition, appraisal and access provision of archive materials. The availability of archives materials reinforces the community by aiding to protect legal rights and prevent human rights violations. It was, thus, necessary to establish whether the National Archives of South Africa is actively building the archives collections that are important for human rights promotion and protection.

In the current era of Industry 4.0, the manufacturing industries are striving toward mass production with mass customization by considering human–robot collaboration. This study aims to propose the reconfiguration of assembly systems by incorporating multiple humans with robots using a human–robot task allocation (HRTA) to enhance productivity.

A human–robot task scheduling approach has been developed by considering task suitability, resource availability and resource selection through multicriteria optimization using the Linear Regression with Optimal Point and Minimum Distance Calculation algorithm. Using line-balancing techniques, the approach estimates the optimum number of resources required for assembly tasks operating by minimum idle time.

The task allocation schedule for a case study involving a punching press was solved using human–robot collaboration, and the approach incorporated the optimum number of appropriate resources to handle different types of proportion of resources.

This proposed work integrates the task allocation by human–robot collaboration and decrease the idle time of resource by integrating optimum number of resources.

This study aims to empirically analyse the influence mechanism of perceived interactivity in real estate APP which affects consumers' psychological well-being. With the growing application of human–machine interaction in real estate APP, it is crucial to utilize human–machine interaction to stimulate perceived interactivity between humans and machines to positively impact consumers' psychological well-being and sustainable development of real estate APP. However, it is unclear whether perceived interactivity improves consumers' psychological well-being.

This study proposes and examines a theoretical model grounded in the perceived interactivity theory, considers the relationship between perceived interactivity and consumers' psychological well-being and explores the mediating effect of perceived value and the moderating role of privacy concerns. It takes real estate APP as the research object, analyses the data of 568 consumer samples collected through questionnaires and then employs structural equation modelling to explore and examine the proposed theoretical model of this study.

The findings are that perceived interactivity (i.e. human–human interaction and human–information interaction) positively influences perceived value, which in turn affects psychological well-being, and that perceived value partially mediates the effect of perceived interaction on psychological well-being. More important findings are that privacy concerns not only negatively moderate human–information interaction on perceived value, but also negatively moderate the indirect effects of human–information interaction on users' psychological well-being through perceived value.

This study expands the context on perceived interaction and psychological well-being in the field of real estate APP, validating the mediating role and boundary conditions of perceived interactivity created by human–machine interaction on consumers' psychological well-being, and suggesting positive implications for practitioners exploring human–machine interaction technologies to improve the perceived interaction between humans and machines and thus enhance consumer psychological well-being and span sustainable development of real estate APP.

The purpose of this paper is to design a new trajectory error compensation method to improve the trajectory tracking performance and compliance of the knee exoskeleton in human–exoskeleton interaction motion.

A trajectory error compensation method based on admittance-extended Kalman filter (AEKF) error fusion for human–exoskeleton interaction control. The admittance controller is used to calculate the trajectory error adjustment through the feedback human–exoskeleton interaction force, and the actual trajectory error is obtained through the encoder feedback of exoskeleton and the designed trajectory. By using the fusion and prediction characteristics of EKF, the calculated trajectory error adjustment and the actual error are fused to obtain a new trajectory error compensation, which is feedback to the knee exoskeleton controller. This method is designed to be capable of improving the trajectory tracking performance of the knee exoskeleton and enhancing the compliance of knee exoskeleton interaction.

Six volunteers conducted comparative experiments on four different motion frequencies. The experimental results show that this method can effectively improve the trajectory tracking performance and compliance of the knee exoskeleton in human–exoskeleton interaction.

The AEKF method first uses the data fusion idea to fuse the estimated error with measurement errors, obtaining more accurate trajectory error compensation for the knee exoskeleton motion control. This work provides great benefits for the trajectory tracking performance and compliance of lower limb exoskeletons in human–exoskeleton interaction movements.