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This study expanded the model of technology acceptance and investigated how the relationship between usefulness, ease of use, efficiency, personalization, safety and security and behavioural intention differ on Gen Z and silver tourists toward smart hotel. This study further applies multiple group analysis to examine whether there are substantial differences among these two groups of respondents.

Using an online survey, this study was undertaken with Gen Z and silver tourists in mainland China who had stayed in smart hotel over the past 12 months. A total of 474 valid responses were collected. Structural equation modelling and multigroup analysis were employed to test the proposed relationships.

This study revealed that personalization did not affect the behavioural intention among Gen Z tourists, meanwhile, there is no positive relationship between usefulness, efficiency and behavioural intention on silver group. Additionally, the findings revealed that there are no substantial differences among Gen Z (digital natives) and silver customers (digital immigrants) regarding smart hotel behavioural intentions.

This study offers strategic guidance for hotel managers to design and reposition smart hotel based on different customer sectors. Further, important implications for smart devices manufacturers are also provided to improve the functioning of hotel service robots.

This is the first study to compare the drivers and outcomes of behavioural intentions among different age groups of tourists toward smart hotels.

The underground logistics system (ULS) is noted to be an innovative delivery alternative that confers benefits such as improved logistics efficiency, reduced traffic congestion and better environmental protection for society. Consumer acceptance is crucial for the widespread application of ULS. Hence, this study aims to explore the drivers affecting consumers’ willingness to adopt an ULS anchored on the uses and gratification theory.

An online survey was implemented among 551 Singapore citizens and structural equation modeling was adopted to examine the theoretical model.

The findings suggest that most gratification variables (i.e. hedonic gratification, environmental protection gratification (EPG) and social gratification), mediated by perceived well-being and conscious attention, have significant effects on consumer adoption of the ULS. Moreover, as shown in the results of total effects, well-being perception exerts the largest impact on consumers’ adoption of ULS, followed by conscious attention, EPG, social gratification, hedonic gratification and convenience gratification.

This study contributes to enriching current theoretical research on consumers’ willingness to accept ULS, and providing several practical implications for logistic service providers and the government to promote consumers’ adoption of ULS.

The imperative for sustainable energy systems is increasingly pressing as the world transitions toward renewable energy sources. Among these, triboelectric nanogenerators (TENGs) have emerged as a viable option for wind energy harvesting. However, they face significant challenges, including material durability under varying wind conditions; the intricacy of material selection and performance; and the trade-off between wear resistance and triboelectric efficiency. This study aims to address the above issues.

Herein, a mode-switch TENG (MS-TENG) was designed to overcome these limitations and serve as a self-powered energy solution for Internet of Things (IoT) sensor networks. The MS-TENG incorporates a multi-stage functional layer and an automatic mode-switching mechanism between contact and non-contact operation, thereby enhancing both efficiency and durability.

It is demonstrated that the MS-TENG achieves a maximum instantaneous output power of 0.069 mW with minimal mechanical wear, effectively capturing wind energy. Its capability to charge capacitors and power a range of electronic devices, such as temperature and humidity sensors, electronic watches and water immersion guards, underscores its practical utility across diverse settings.

This research situates the MS-TENG as a pioneering technology in smart sensor applications for future energy-harvesting endeavors, optimizing energy acquisition under fluctuating wind conditions and reinforcing the sustainability of IoT networks.

Deep learning techniques are unavoidable in a variety of domains such as health care, computer vision, cyber-security and so on. These algorithms demand high data transfers but require bottlenecks in achieving the high speed and low latency synchronization while being implemented in the real hardware architectures. Though direct memory access controller (DMAC) has gained a brighter light of research for achieving bulk data transfers, existing direct memory access (DMA) systems continue to face the challenges of achieving high-speed communication. The purpose of this study is to develop an adaptive-configured DMA architecture for bulk data transfer with high throughput and less time-delayed computation.

The proposed methodology consists of a heterogeneous computing system integrated with specialized hardware and software. For the hardware, the authors propose an field programmable gate array (FPGA)-based DMAC, which transfers the data to the graphics processing unit (GPU) using PCI-Express. The workload characterization technique is designed using Python software and is implementable for the advanced risk machine Cortex architecture with a suitable communication interface. This module offloads the input streams of data to the FPGA and initiates the FPGA for the control flow of data to the GPU that can achieve efficient processing.

This paper presents an evaluation of a configurable workload-based DMA controller for collecting the data from the input devices and concurrently applying it to the GPU architecture, bypassing the hardware and software extraneous copies and bottlenecks via PCI Express. It also investigates the usage of adaptive DMA memory buffer allocation and workload characterization techniques. The proposed DMA architecture is compared with the other existing DMA architectures in which the performance of the proposed DMAC outperforms traditional DMA by achieving 96% throughput and 50% less latency synchronization.

The proposed gated recurrent unit has produced 95.6% accuracy in characterization of the workloads into heavy, medium and normal. The proposed model has outperformed the other algorithms and proves its strength for workload characterization.

The increasing popularity of live-streaming commerce has provided a new opportunity for e-retailers to boost sales. This study integrated signaling theory and social exchange theory to investigate how streamer- and product-centered signals influence customers’ likelihood of making an impulsive purchase in the live-streaming commerce context.

An online survey was designed and distributed to the target respondents in China using purposive sampling. A total of 735 valid responses were analyzed with partial least square structural equation modeling (PLS-SEM).

Both streamer-centered signals, i.e. streamer credibility and streamer interaction quality, were discovered to significantly influence product-centered signal, i.e. product information quality. Additionally, streamer interaction quality was found to have a significant impact on streamer credibility. Furthermore, it was observed that customer engagement played a significant mediating role in the relationship between product information quality and impulsive buying tendency. Moreover, the paths between product information quality and customer engagement, as well as the connection between engagement and impulsive buying tendency, were found to be moderated by guanxi orientation.

Despite the prevalence of impulsive purchases in live-streaming commerce, few studies have empirically investigated the impact of streamer and product signals on influencing customers’ impulsive purchase decisions. Consequently, to the best of our knowledge, this study distinguishes itself by offering empirical insights into how streamers use reciprocating relationship mechanisms to communicate signals that facilitate impulsive purchase decisions.

In social media, crisis information susceptible of generating different emotions could be spread at exponential pace via multilevel super-spreaders. This study aims to interpret the multi-level emotion propagation in natural disaster events by analyzing information diffusion capacity and emotional guiding ability of super-spreaders in different levels of hierarchy.

We collected 47,042 original microblogs and 120,697 forwarding data on Weibo about the “7.20 Henan Rainstorm” event for empirical analysis. Emotion analysis and emotion network analysis were used to screen emotional information and identify super-spreaders. The number of followers is considered as the basis for classifying super-spreaders into five levels.

Official media and ordinary users can become the super-spreaders with different advantages, creating a new emotion propagation environment. The number of followers becomes a valid basis for classifying the hierarchy levels of super-spreaders. The higher the level of users, the easier they are to become super-spreaders. And there is a strong correlation between the hierarchy level of super-spreaders and their role in emotion propagation.

This study has important significance for understanding the mode of social emotion propagation and making decisions in maintaining social harmony.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-03-2024-0192.

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 purpose of this study is to review the literature on the adoption determinants of mobile health (M-health) applications for health behavior change following a systematic methodology.

This review systematically identified 134 peer-reviewed studies out of 10,687 from Scopus, Web of Science, PubMed and the Association for Computing Machinery (ACM) published between 2010 and 2021. This review used a thematic analysis to produce the main themes conceptualizing a holistic framework of the investigated M-health application adoption factors.

Despite the exploration of multifaceted adoption determinants and behaviors, the current publications exhibit limitations. The studies not only show a lack of representation of multiple health behaviors and medical conditions but also fail to involve data from low- and middle-developing countries, where M-health application utilization is crucial. Findings revealed that there is a considerable absence of a solid theoretical foundation that unveils a gap in interpreting the adoption factors effectively. Understanding cultural and demographic variances and exploring financial factors and healthcare provider involvement is essential for tailoring M-health application interventions. Continuous assessment of technological factors and evaluation of the actual impact of M-health application usage on behavioral changes and health outcomes will further enhance the effectiveness and adoption of these technologies.

This review is one of the first comprehensive reviews of determinants of M-health application adoption targeting health behavior change for the general public and patients.

The purpose of this paper is to overcome the inherent lack of precision in commonly used interpolation procedures when solving the mathematical model of turbofan engines, as well as to address the issue that the theoretical variogram model in traditional Kriging models is prone to subjective selection bias, which makes it impossible to accurately capture the inherent fluctuation patterns in compressor data.

To mitigate this challenge, based on the spatial distribution characteristics of the compressor characteristic data of a certain type of turbofan engine, the input and output dimensions of the model are defined. By determining the stable operating region from the original component data, the authors use the proposed Kriging method improved with a support vector machine model to reconstruct the characteristics at unknown speeds within this region. The effectiveness of the proposed method is evaluated using the established assessment metrics.

Experimental results demonstrate that the proposed method exhibits significant advantages over the conventional Kriging approach. Specifically, it leads to a substantial reduction in root mean square error and mean absolute error by 0.0153/0.0118 (low speed), 0.1306/0.0362 (medium speed) and −0.0066/0.2366 (high speed).

This refined approach not only offers notable engineering applicability but also contributes significantly to the enhancement of aerospace engine model solutions’ precision.

The post-pandemic era has shifted most industries, businesses and consumers online, increasing the demand for electronic devices, mainly laptops. Additionally, most non-Western countries inhabit highly religious but cash-strapped individuals, making them a potential market for second-hand laptops. With this in mind, this study aims to explore the effects of lenient return policy (LRP) and religiosity on consumer confidence in retailer (CCR), consumer well-being and purchase intention.

This paper conducted a between-subjects field experiment comparing two return policy conditions (cash return vs. other return) with a sample of 222 participants. Data were analysed using partial least squares structural equation modelling (PLS-SEM) to test the hypothesised relationships, and multigroup analysis (MGA) was employed to assess the experimental effects based on the return policy conditions. The moderating effects of religiosity were also examined. All analyses were conducted using SmartPLS software.

The results confirm that an LRP positively predicts consumer confidence in retailer, well-being and purchase intention. Religiosity had a moderating effect on LRP outcomes. Additionally, the experiment confirmed that consumers experienced better well-being and were more likely to purchase if offered full cashback.

Retailers of second-hand shopping products should offer LRP (full cashback) to foster consumer confidence, well-being and purchase intention. Additionally, for highly religious consumers, aligning return policies with religious principles should further enhance consumer well-being and purchase intention.

This study is among the earliest to investigate the impact of LRP on CCR and well-being. Moreover, a novel attempt is made to explore the moderating effects of religiosity on LRP outcomes. Likewise, a field experiment to validate the greater effects of cashback on consumer well-being and purchase intention adds to the novelty of this study.