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The launch of ChatGPT has brought the large language model (LLM)-based generative artificial intelligence (GAI) into the spotlight, triggering the interests of various stakeholders to seize the possible opportunities implicated by it. Nevertheless, there are also challenges that the stakeholders should observe when they are considering the potential of GAI. Given this backdrop, this study presents the viewpoints gathered from various subject experts on six identified areas.

Through an expert-based approach, this paper gathers the viewpoints of various subject experts on the identified areas of tourism and hospitality, marketing, retailing, service operations, manufacturing and healthcare.

The subject experts first share an overview of the use of GAI, followed by the relevant opportunities and challenges in implementing GAI in each identified area. Afterwards, based on the opportunities and challenges, the subject experts propose several research agendas for the stakeholders to consider.

This paper serves as a frontier in exploring the opportunities and challenges implicated by the GAI in six identified areas that this emerging technology would considerably influence. It is believed that the viewpoints offered by the subject experts would enlighten the stakeholders in the identified areas.

Current multi-source image fusion methods frequently overlook the issue of detailed features when employing deep learning technology, resulting in inadequate target feature information. In real-world mission scenarios, such as military information acquisition or medical image enhancement, the prominence of target feature information is of paramount importance. To address these challenges, this paper introduces a novel infrared-visible light fusion model.

Leveraging the foundational architecture of the traditional DenseFuse model, this paper optimizes the backbone network structure and incorporates a Unique Feature Encoder (UFE) to meticulously extract the distinctive features inherent in the two images. Furthermore, it integrates the Convolutional Block Attention Module (CBAM) and the Squeeze and Excitation Network (SE) to enhance and replace the original spatial and channel attention mechanisms.

Compared to other methods such as IFCNN, NestFuse, DenseFuse, etc., the values of entropy, standard deviation, and mutual information index of the method presented in this paper can reach 6.9985, 82.6652, and 13.6022, respectively, which are significantly improved compared with other methods.

This paper presents a UFEFusion framework that synergizes with the CBAM attention mechanism to markedly augment the extraction of detailed features relative to other methods. Moreover, the framework adeptly extracts and amplifies unique features from disparate images, thereby elevating the overall feature representation capability.

The purpose of this study is to reveal the significant contribution of MXene on enhancing tribological properties and to obtain the influence mechanism of various factors on friction characteristics of rolling bearing under extreme conditions.

Under extreme working conditions, the friction characteristics of rolling bearings directly determine the safety and reliability of the transmission system. In this study, MXene is added to the origin lubricating grease (OLG) of rolling bearing to enhance their friction characteristics. Then, the effects of inner ring speed, radial load, grease filling volume and other factors on the friction coefficient of rolling bearing are analyzed using the Taguchi method.

The results indicate that the ranking of various factors affecting the friction coefficient is: radial load, inner ring speed, MXene additive content in grease and grease filling volume. Especially, the radial load and inner ring speed exhibit extremely significant effects, while the MXene additive content in grease (P < 0.05) has a significant influence on the friction coefficient of rolling bearing. The optimal condition for rolling bearing lubricated with MXene additives lubricating grease (MALG) achieves the lowest friction coefficient of 0.0049 under 1,000 rpm, 9 kN and 35% grease filling volume.

This study could offer reference solution for utilizing MXene nano-lubrication to fufill the demands of precision, heavy-load, or long-lifespan bearings. Furthermore, the lubrication approach has the potential to be expanded into aerospace, defense, and various industrial fields, thereby significantly promoting its practial engineering applications.

Given the substantial contribution of intangible assets to firms’ overall valuation in the present knowledge-based era, this study aims to explore how these non-physical assets influence the effectiveness of ESG disclosure in reducing carbon intensity (CI).

This study focuses on the S&P 500 firms for the period 2015–2016 to 2022–2023 to study the overall impact of ESG disclosure on CI and investigate the differential impact of the same based on firms’ intangible assets by applying the system generalized method of moments (GMM) and two stage least squares (2SLS) regression models.

This study finds that overall, there is a significant negative impact of ESG disclosure on CI. However, the impact of ESG disclosure on CI varies based on firms’ intangible assets. In particular, ESG disclosure leads to significant mitigation of CI for firms with high intangible assets, while the same relationship becomes insignificant for the firms with low intangible assets.

Given the extant ESG literature that largely focuses on its financial outcome, this study makes a novel contribution by studying the impact of ESG disclosure on a sustainability parameter, that is CI in the context of the US. Moreover, this research adds deeper insights into the strategic importance of intangible assets in enhancing corporate sustainability performance.

This study proposed an extended theory of planned behaviour (TPB), that is, considering that behavioural beliefs, normative beliefs and control beliefs (beliefs) will influence perceived service quality (PSQ) on food delivery services. PSQ (behavioural intention) will influence electronic word-of-mouth (EWOM) (behaviour). In addition, exogenous variables including information from online ratings and consumer groups will affect the strength of the relationship between received service quality and EWOM on food delivery service.

This study aimed to investigate the mediation (PSQ) and moderation (Online ratings and consumer groups) effects on the extended TPB for Taiwanese consumers (n = 823).

This study first found a positive relationship between different beliefs and PSQ (behavioural intention). In addition, there is a positive relationship between PSQ and EWOM. Online rating has a moderating effect between PSQ and EWOM. Consumer group has a moderating relationship between PSQ and EWOM.

This study first found that the three stages of beliefs-intention-behaviour for consumers on food delivery service are reciprocal with two paths, starting with offline-to-online in terms of generating the positive relationship between individual belies and PSQ. Next, it can generate positive power to return online with a behaviour of EWOM. In addition, online ratings can enhance and strengthen the positive effect between PSQ and EWOM.

This study aims to propose a comprehensive framework for assessing circular economy (CE) performance in the agri-food sector. It identifies those indicators that should be used to measure circularity regarding CE strategies, applies them at a microeconomic level and examines the impact of key business determinants (financial, internalization, knowledge, awareness and digitalization).

A literature review was conducted with a focus on CE strategies and related indicators. The framework obtained is applied on a Spanish agri-food sector survey to provide an empirical CE analysis at firm level. A partial least squares based-structural equation modelling method is applied.

The research suggests a set of circularity indicators to assess CE strategies at micro level. The findings reveal the heterogeneity of CE sub-indicators related to narrowing, slowing, closing and regenerating strategies. It also highlights the strong effects that the drivers tested have on CE, especially awareness and knowledge.

This study provides a framework that can guide public policies and business decisions in sectors with significant environmental impacts. It provides a conceptual framework that explains which CE indicators should be considered by governments and regulators when developing policies that seek to promote circular strategies.

In the case of companies, the results show that acquiring more environmental awareness and knowledge is essential for promoting more sustainable work habits that enhance CE.

The paper offers a novel contribution to the CE literature by introducing a multidimensional indicator framework comprehensively applied to the agri-food sector, integrating miscellaneous pillars of circular strategies and business determinants.

Nano graphitic-carbon nitride (g-C₃N₄) is an emerging lubrication technology with excellent performance and significant potential for future applications. This study aims to investigate the effect of nano g-C₃N₄ as a lubricant additive on the wear performance of bearing steel disk.

Various mass fractions of g-C₃N₄ were introduced into the base oil. Combining tribological testing, rheological testing and surface analysis methods, the anti-wear properties and lubrication mechanisms were analyzed.

Transmission electron microscopy images revealed that the size of the nanoparticles of g-C₃N₄ ranges from 10 to 100 nm. Phase analysis of the g-C₃N₄ sample was conducted using X-ray diffraction. Further, 1.0% mass fraction of g-C₃N₄ in the base oil provides excellent anti-wear and friction-reducing performance. Compared to the base oil alone, it reduces the average friction coefficient by 63.8% and decreases the wear rate by 43.1%, significantly reducing the depth and width of the wear scar. Energy-dispersive X-ray spectroscopy and scanning electron microscope analysis revealed that the oil sample containing nano g-C₃N₄ can form a lubricating film on the sliding surface of bearing steel after wear, which enhances the lubricating properties of the base oil.

The synergistic effect of the base oil and nanoparticles reduces friction and wear and is expected to extend the service life of bearing steel. These findings suggest that incorporating nano g-C₃N₄ as a lubricant additive offers significant potential for improving the performance of mechanical components.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2024-0456/

This study evaluated the effect of functional jackets designed to reduce the workload on the upper extremities and boost the muscle strength of workers from prolonged repetitive work.

The functional jacket designs for the upper limb incorporated a dynamic taping line with an elastic band, three-dimensional support for the abdomen and posterior waist (S2) and an X-shaped line on the scapular region combined with abdominal and posterior waist support (S3). Clothing pressure was adjusted ergonomically for different areas. A standard jacket served as the control (S1). The designs were evaluated based on work posture, electromyography and subjective sensation.

S2 and S3 effectively reduced the workload on the upper extremities by decreasing the trapezius muscle, shoulders and waist movement. S3 showed a low muscle-activation level on electromyogram analysis and was excellent in subjective sensation.

A functional jacket with optimal compression applied differentially to the upper extremities, with three-dimensional abdominal and posterior waist support and an X-shaped line on the scapula region, can reduce the workload on the upper extremities and boost the core muscle strength to improve work performance.

This study aims to investigate the information-seeking behaviours of home buyers – primarily owner-occupants – using digital real estate platforms, a key element in the industry’s shift towards digital services. It focuses on first-time buyers and repurchasers to examine how these platforms assist in the home-buying process and influence buyer behaviour in Taiwan.

A mixed methods approach was adopted, combining quantitative surveys and qualitative interviews to gather comprehensive data on user experiences and preferences.

The research identifies brand perception, search functionality and search results as critical factors influencing platform usage. Furthermore, it reveals an increasing demand for innovative artificial intelligence-driven search features to enhance user experience and platform convenience, reflecting evolving user expectations.

By addressing the specific context of Taiwan’s real estate market, this study provides novel insights into the interplay between digital platform features and user behaviour. The findings offer practical recommendations for improving platform design to better align with user needs.

This study investigates the moderating effects of consumers’ occupations on their purchase intentions (PIs) for food takeout services using a modified unified theory of acceptance and use of technology model. It evaluates how different occupations influence the relationships between social influence (SI), expectation confirmation (EC), facilitating conditions (FC) and PI.

The study collected data from individuals in various occupations, including technical/associate professionals, executives/professionals, administrative/service workers and manual/operative workers. The data were analyzed using structural equation modeling, while hierarchical analysis assessed how occupation moderated the relationships between latent variables (SI, EC and FC) and PI.

Different occupations have a certain moderating effect on the relationships between SI/EC/FC and PI. For the technical and associate professionals and manual and operative occupations, the moderating effect of FC on PI is stronger than that of EC and SI. For executives and professionals and administrative and service occupations, the moderating effect of EC on PI is stronger than that of SI and FC.

This study provides new insights into how occupational differences influence consumer behavior in the context of online food ordering services. The results expand the application of the unified theory of acceptance and use of technology model and the understanding of the influence of occupation on consumer’ behavior.