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This study aims to examine the ability of Generative Pre-trained Transformer 4 (GPT-4), one of the most powerful large language models, to generate a literature review for peer-reviewed journal publications. The objective is to determine whether business scholars can rely on GPT-4’s assistance with literature reviews and how the nature of human–artificial intelligence (AI) interaction may affect the quality of the reviews generated by GPT-4.

A survey of 30 experienced researchers was conducted to assess the quality of the literature reviews generated by GPT-4 in comparison with a human-authored literature review published in a Social Science Citation Index (SSCI) journal. The data collected were then analyzed with analysis of variance to ascertain whether we may trust GPT-4’s assistance in writing literature reviews.

The statistical analysis reveals that when a highly structured approach being used, GPT-4 can generate a high-quality review comparable to that found in an SSCI journal publication. However, when a less structured approach is used, the generated review lacks comprehensive understating and critical analysis, and is unable to identify literature gaps for future research, although it performed well in adequate synthesis and quality writing. The findings suggest that we may trust GPT-4 to generate literature reviews that align with the publication standards of a peer-reviewed journal when using a structured approach to human–AI interaction.

The findings suggest that we may trust GPT-4 to generate literature reviews that align with the publication standards of a peer-reviewed journal when using a structured approach to human–AI interaction. Nonetheless, cautions should be taken due to the limitations of this study discussed in the text.

By breaking down the specific tasks of a literature review and using a quantitative rather than qualitative assessment method, this study provides robust and more objective findings about the ability of GPT-4 to assist us with a very important research task. The findings of this study should enhance our understanding of how GPT-4 may change our research endeavor and how we may take a full advantage of the advancement in AI technology in the future research.

With the accumulation of theoretical research and practical experience in the field of garment production research, it is imperative to methodically analyze and reflect on the achievements that have been made. This review aims to systematically map the academic landscape of research articles on garment production, elucidate the evolutionary trajectory of this discipline, identify emerging research frontiers and provide insights into its prospects.

Based on the Web of Science core database, 307 research articles were systematically analyzed by CiteSpace software. The study employed bibliometric and thematic analyses to offer in-depth insights into the dynamics and evolution of research on garment production.

Results reveal that keyword analysis emphasizes the significance of topics such as apparel assembly line, lean production, circular economy, fuzzy logic, global production networks, social sustainability and supply chain management in garment production research. Citation analysis demonstrates that articles related to environmental impact, supply chain management, production process and production technology constitute the knowledge base and core of garment production research. Eight principal research themes emerge: customized garment production, production technology, quality assurance, equipment, production lines, supply chain management, environmental impact and social and human impact. Future research hotspots will focus more on sustainable, intelligent and digital clothing production.

The findings systematically sort out the hotspots and trends in garment production, establish knowledge structures and display them through intuitive representations. The rich insights set the stage for the development of garment production and provide future guidance for theoretical research.

This study aims to streamline and enhance the assembly defect inspection process in diesel engine production. Traditional manual inspection methods are labor-intensive and time-consuming because of the complex structures of the engines and the noisy workshop environment. This study’s robotic system aims to alleviate these challenges by automating the inspection process and enabling easy remote inspection, thereby freeing workers from heavy fieldwork.

This study’s system uses a robotic arm to traverse and capture images of key components of the engine. This study uses anomaly detection algorithms to automatically identify defects in the captured images. Additionally, this system is enhanced by digital twin technology, which provides inspectors with various tools to designate components of interest in the engine and assist in defect checking and annotation. This integration facilitates smooth transitions from manual to automatic inspection within a short period.

Through evaluations and user studies conducted over a relatively long period, the authors found that the system accelerates and improves the accuracy of engine inspections. The results indicate that the system significantly enhances the efficiency of production processes for manufacturers.

The system represents a novel approach to engine inspection, leveraging robotic technology and digital twin enhancements to address the limitations of traditional manual inspection methods. By automating and enhancing the inspection process, the system offers manufacturers the opportunity to improve production efficiency and ensure the quality of diesel engines.

This study aims to investigate the mediating role of green employee empowerment (GEE) in the relationship between green discipline management and green involvement with green employee creativity and innovation (GECI) within public universities in Yemen. The research explores how the components of green human resource management (GHRM) influence green creativity and innovation through employee empowerment.

The study utilizes both primary and secondary data. The primary data were gathered through structured questionnaires distributed to 363 employees in public universities in Yemen. The data were analyzed using structural equation modeling (SEM) with AMOS to assess the relationships among the variables.

The findings reveal that green discipline management and green involvement have a significantly positive impact on GEE and GECI. Furthermore, GEE partially mediates this relationship, which demonstrates that empowered employees are more likely to contribute to environmentally sustainable creativity and innovation.

This research helps better understand how GHRM practices, such as discipline management and involvement, can promote green creativity and innovation through employee empowerment. By examining the mediating role of GEE, the study provides valuable insights for academic institutions and organizational leaders seeking to integrate sustainability into human resource (HR) practices and policies.

This study aims to provide a timely review of hospitality and tourism live streaming (HTLS) research, aiming to understand the intellectual foundation in the past and identify opportunities for future research.

This study collected 56 papers from journals in the Social Science Citation Index database or the Australian Business Deans Council journal quality list and adopted a hybrid systematic literature review combining bibliometric analysis and the theory-context-characteristics-method framework-based approach. Additionally, a cross-tabulation analysis was conducted to compare the research on two specific forms of HTLS – tourism e-commerce live streaming (TEcLS) and travel live streaming.

The study found that the current research lacks in-depth theoretical insights, focuses on China as the national context and viewers as the research subject, mainly uses online surveys and lacks first-order knowledge. Moreover, it identified five research themes, including the impact of HTLS on viewer behavior, purchase behavior in HTLS and introduction of HTLS, within which eight categories of research constructs, such as viewer-related, streamer-related and relationship-related, are applied.

In terms of research, the study reveals various gaps in the existing literature and provides potential research directions to advance the field. As for practice, it offers valuable strategies for practitioners to optimize their marketing efforts using HTLS.

This study provides an up-to-date review of the HTLS field through a systematic and reproducible method. It offers critical discussions and suggestions for academic research and industry practices.

The current study aims to investigate how young smartphone users in Bangladesh relate their brand experiences to brand loyalty. In addition, we want to visualize the direct and mediating effects of brand satisfaction, brand love and brand advocacy in our model.

The researchers examined the hypotheses by employing structural equation modeling (SEM) in AMOS and Decision Analyst STATS, version 2.0, with a sample size of 470 Bangladeshi smartphone users. The authors constructed the conceptual model by drawing upon both theoretical and empirical foundations. The researchers obtained data by utilizing an adopted and self-administered pre-structured questionnaire distributed via an online platform.

The results showed that brand experience greatly influences brand satisfaction, love, advocacy and loyalty, all of which have a significant impact on users’ brand loyalty across the country. The findings also suggested that the function of brand satisfaction as a critical mediator in the link between brand experience and brand loyalty was significant.

This experiment contributes to the body of knowledge by focusing on emotional brand attachments like brand satisfaction, love and advocacy and proposing that they can mediate experience and loyalty in the mobile market. The study also helps managers and executives better understand the primary drivers of smartphones, which are essential for generating and sustaining consumers’ happiness and loyalty in today’s highly competitive consumer market.

Damage of engineering structures is a nonlinear evolutionary process that spans across both material and structural levels, from mesoscale to macroscale. This paper aims to provide a comprehensive review of damage analysis methods at both the material and structural levels.

This study provides an overview of multiscale damage analysis of engineering structures, including its definition and significance. Current status of damage analysis at both material and structural levels is investigated, by reviewing damage models and prediction methods from single-scale to multiscale perspectives. The discussion of prediction methods includes both model-based simulation approaches and data-driven techniques, emphasizing their roles and applications. Finally, summarize the main findings and discuss potential future research directions in this field.

In the material level, damage research primarily focuses on the degradation of material properties at the macroscale using continuum damage mechanics (CDM). In contrast, at the mesoscale, damage research involves analyzing material behavior in the meso-structural domain, focusing on defects like microcracks and void growth. In structural-level damage analysis, the macroscale is typically divided into component and structural scales. The component scale examines damage progression in individual structural elements, such as beams and columns, often using detailed finite element or mesoscale models. The structural scale evaluates the global behavior of the entire structure, typically using simplified models like beam or shell elements.

To achieve realistic simulations, it is essential to include as many mesoscale details as possible. However, this results in significant computational demands. To balance accuracy and efficiency, multiscale methods are employed. These methods are categorized into hierarchical approaches, where different scales are processed sequentially, and concurrent approaches, where multiple scales are solved simultaneously to capture complex interactions across scales.

The advent of artificial intelligence (AI) in the accounting landscape marks a significant shift, promising gains in efficiency and accuracy but also eliciting concerns about job displacement (JD) and broader socio-economic implications. This study aims to provide an in-depth understanding of how AI’s integration in accounting contributes to JD, reshapes decision-making processes and reverberates across economic and social dimensions. It also offers evidence-based policy recommendations to mitigate adverse outcomes.

Leveraging a cross-sectional survey disseminated through Facebook, this research used snowball sampling to target a diverse cohort of accounting professionals. The collected data were subjected to meticulous analysis through descriptive and regression models, facilitated by SmartPLS 4 software.

The analysis revealed a significant correlation between AI’s increasing role in accounting and a heightened rate of JD. This study found that this displacement is not isolated; it has tangible repercussions on decision-making paradigms, economic well-being, professional work dynamics and social structures. These insights corroborate existing frameworks, including, but not limited to, theories of technological unemployment and behavioural adjustments.

Although providing valuable insights, this study acknowledges limitations such as the restricted sample size, the cross-sectional nature of the survey and the inherent biases of self-reported data. Future research could aim to extend these initial findings by adopting a longitudinal approach and potentially integrating external data sources.

As AI technology becomes increasingly ingrained in accounting practices, there is an urgent need for coordinated action among stakeholders. Policy recommendations include focused efforts on talent retention, investment in upskilling programs and the establishment of support mechanisms for those adversely affected by AI adoption.

By synthesising a range of theoretical perspectives, this study offers a comprehensive exploration of AI’s multi-dimensional impacts on the accounting profession. It stands out for its nuanced examination of JD and its economic and social implications, thereby contributing to both academic discourse and policy formulation. This work serves as an urgent call to action, highlighting the need for strategies that both exploit AI’s potential benefits and protect the workforce from its disruptive impact.

Governments worldwide are placing a greater emphasis on enhancing ecology and the environment as a result of escalating ecological issues. One possible approach is sustainable governance. This chapter explores the interrelated roles of internal control, environmental accounting, and environmental auditing mechanisms in promoting sustainable governance and green transformation. By looking at these three aspects, the chapter illustrates how integrated approaches can promote sustainable practices and guarantee adherence to environmental standards. The objective of this chapter is to present a thorough knowledge of the ways in which these components work together to support sustainability as a whole.

In the practice of live streaming e-commerce, the consumer demand is usually uncertain, and the inventory and prices can be decided by brand owners or streamers. To this end, this study examines the inventory and pricing decisions of the brand owner and streamer in a live streaming e-commerce supply chain under demand uncertainty.

In this study, four scenarios are considered, i.e. the brand owner determines the inventory and price (Scenario BB), the brand owner determines the inventory and the streamer determines the price (Scenario BS), the streamer determines the inventory and the brand owner determines the price (Scenario SB), and the streamer determines the inventory and price (Scenario SS).

The results show that the inventory and prices, as well as the profits of the brand owner and streamer increase with the consumer sensitivity to streamer’s sales effort level under the four scenarios. The inventory (price) is the highest under Scenario SS (SB), while that is the lowest under Scenario BB (BS). In addition, when the sensitivity is low, the brand owner’s profit is the highest under Scenario BB, otherwise, the profit is the highest under Scenario SS. Regardless of the sensitivity, the streamer’s profit is always the highest under Scenario SS.

Few studies focused on the inventory and pricing decisions of brand owners and streamers in live streaming e-commerce supply chains under demand uncertainty, while this work bridges the research gap. This study can provide theoretical basis and decision support for brand owners and streamers.