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Using Generation Z consumers from China as an example and focusing on the nostalgia-driven design of brand spokes-character, this study sought to update research on the causal relationship between nostalgia and brand attitude for younger consumers. Two types of nostalgic brand spokes-characters (i.e., eliciting personal nostalgia and historical nostalgia) were examined separately and compared to verify their contributions to more positive brand attitude, as well as related mechanisms, that is, whether consumer trust in the spokes-character mediated the relationship between nostalgic spokes-characters and brand attitude.

An experiment was first conducted to test the causal effects of brand spokes-characters designed to elicit two types of nostalgic feelings (i.e., personal nostalgia and historical nostalgia). Then, the authors investigated the influencing mechanism of nostalgic brand spokes-characters based on bootstrap mediation models.

The results revealed that for less familiar brand spokes-characters, either type of nostalgia-driven design would enhance consumers' brand attitude. Moreover, consumer trust in the spokes-character mediated the relationship between personal-nostalgic brand spokes-characters and brand attitude.

This study was the first to examine personal nostalgia and historical nostalgia separately regarding the effects of nostalgic spokes-characters and related mechanisms. By combining methods of experimental design and bootstrap mediation modeling, it provided a more robust evaluation of nostalgia-driven design, and supported using certain nostalgic styles for designing brand spokes-characters, which can help modern brands draw more interest from young consumers and promote more positive brand attitude.

The present study aims to examine the households’ attitudes and intentions to adopt an indoor air purifier against the smog crisis in India by using a comprehensive theoretical framework based on the combination of the Protective Action Decision Model (PADM) and the Theory of Planned Behavior (TPB). The United Nations Sustainable Development Goals (SDGs) 2030 also emphasized ensuring a healthy and safe life, especially by achieving SDG-3, SDG-11 and SDG-13.

Using purposive sampling, the data were collected through a survey questionnaire distributed to 382 households, and study hypotheses were assessed by using partial least squares structural equation modeling employing SmartPLS.

The results revealed that mental health risk perception (MHRP) was the most influential determinant of households’ attitudes toward adopting air purifiers, followed by smog knowledge, physical health risk perception (PHRP), information seeking and product knowledge. Notably, results revealed that households’ attitude is a leading determinant of their adoption intention toward the air purifier compared to subjective norms (SN) and perceived behavioral control (PBC).

To the best of the authors’ knowledge, the present study is the first to provide new insights into an individual’s protective behavior response toward ecological hazards by examining the households’ adoption intention toward the air purifier against the smog crisis using PADM and TPB model inclusively. In addition, the present study analyzes the impact of both PHRP and MHRP on individuals’ protective behavior separately. Also, this study provides theoretical contributions and important practical implications for the government, manufacturers and air purifier sellers.

This paper aims to empirically investigate the impact of the carbon intensity constraint policy (CICP) on green innovation.

This study takes the implementation of the CICP as a quasi-natural experiment and uses a quasi–difference-in-difference method to investigate the impact of the CICP on firm green innovation from a microeconomic perspective.

The CICP significantly limits the quality of firms’ green innovation. Among the range of green patents, the CICP distorts only patents related to CO₂ emissions. The inhibitory effect is more pronounced in non-state-owned enterprises and heavily polluting firms. R&D investment and green investor are identified as the main mechanism.

These findings provide evidence for the influence of the CICP on firm green innovation, which can guide policymakers in China and other emerging economies that prioritize carbon intensity constraint targets and the improvement of relevant auxiliary measures.

Governments and firms should have a comprehensive understanding of environmental policies and corporate behavior and need to mitigate the negative impact through a combination of measures.

This study contributes to the literature by providing additional empirical evidence regarding the two opposing sides of the ongoing debate on the positive or negative effects of CICP. It also provides new evidence on the policy effect of the CICP on firm green innovation, together with its mechanisms and heterogeneous influences.

This paper delves into the aerodynamic optimization of a single-stage axial turbine employed in aero-engines.

An efficient integrated design optimization approach tailored for turbine blade profiles is proposed. The approach combines a novel hierarchical dynamic switching PSO (HDSPSO) algorithm with a parametric modeling technique of turbine blades and high-fidelity Computational Fluid Dynamics (CFD) simulation analysis. The proposed HDSPSO algorithm introduces significant enhancements to the original PSO in three pivotal aspects: adaptive acceleration coefficients, distance-based dynamic neighborhood, and a switchable learning mechanism. The core idea behind these improvements is to incorporate the evolutionary state, strengthen interactions within the swarm, enrich update strategies for particles, and effectively prevent premature convergence while enhancing global search capability.

Mathematical experiments are conducted to compare the performance of HDSPSO with three other representative PSO variants. The results demonstrate that HDSPSO is a competitive intelligent algorithm with significant global search capabilities and rapid convergence speed. Subsequently, the HDSPSO-based integrated design optimization approach is applied to optimize the turbine blade profiles. The optimized turbine blades have a more uniform thickness distribution, an enhanced loading distribution, and a better flow condition. Importantly, these optimizations lead to a remarkable improvement in aerodynamic performance under both design and non-design working conditions.

These findings highlight the effectiveness and advancement of the HDSPSO-based integrated design optimization approach for turbine blade profiles in enhancing the overall aerodynamic performance. Furthermore, it confirms the great prospects of the innovative HDSPSO algorithm in tackling challenging tasks in practical engineering applications.

This article attempts to contribute to medical dispute resolution by examining the adoption of medical judicial expertise opinions in determining medical malpractice responsibility and its coordination with the judge’s legal opinions.

This article examines the legal basis and empirical data to demonstrate the decisive effect of medical judicial experts’ opinions in allocating medical malpractice responsibility and corresponding dispute resolution effectiveness.

High reliance on medical judicial expertise in medical dispute litigation not only unifies the judicial standards but also limits judges’ discretion, which brings the risk of contradiction between factual and legal findings, which currently ends in judges’ compromise.

The current medical malpractice provisions neglect the divergence of medical judicial expertise and judges’ opinions in determining medical malpractice responsibility, which produces difficulties in harmonizing awarded compensations and parties’ expectations, leading to problematic medical dispute litigation in Mainland China.

Vision transformers (ViT) detectors excel in processing natural images. However, when processing remote sensing images (RSIs), ViT methods generally exhibit inferior accuracy compared to approaches based on convolutional neural networks (CNNs). Recently, researchers have proposed various structural optimization strategies to enhance the performance of ViT detectors, but the progress has been insignificant. We contend that the frequent scarcity of RSI samples is the primary cause of this problem, and model modifications alone cannot solve it.

To address this, we introduce a faster RCNN-based approach, termed QAGA-Net, which significantly enhances the performance of ViT detectors in RSI recognition. Initially, we propose a novel quantitative augmentation learning (QAL) strategy to address the sparse data distribution in RSIs. This strategy is integrated as the QAL module, a plug-and-play component active exclusively during the model’s training phase. Subsequently, we enhanced the feature pyramid network (FPN) by introducing two efficient modules: a global attention (GA) module to model long-range feature dependencies and enhance multi-scale information fusion, and an efficient pooling (EP) module to optimize the model’s capability to understand both high and low frequency information. Importantly, QAGA-Net has a compact model size and achieves a balance between computational efficiency and accuracy.

We verified the performance of QAGA-Net by using two different efficient ViT models as the detector’s backbone. Extensive experiments on the NWPU-10 and DIOR20 datasets demonstrate that QAGA-Net achieves superior accuracy compared to 23 other ViT or CNN methods in the literature. Specifically, QAGA-Net shows an increase in mAP by 2.1% or 2.6% on the challenging DIOR20 dataset when compared to the top-ranked CNN or ViT detectors, respectively.

This paper highlights the impact of sparse data distribution on ViT detection performance. To address this, we introduce a fundamentally data-driven approach: the QAL module. Additionally, we introduced two efficient modules to enhance the performance of FPN. More importantly, our strategy has the potential to collaborate with other ViT detectors, as the proposed method does not require any structural modifications to the ViT backbone.

Using the integrated behavioural model (IBM) as a theoretical framework, this study aims to identify the determinants of saving behaviour among Malaysia's income-earning Generation Y (Gen Y) born in the years 1980–1995.

The study was conducted using a questionnaire survey targeting Gen Y respondents 500 sets of responses were obtained via convenience sampling method.

Analysis conducted using partial least squares structural equation modelling (PLS-SEM) revealed that there were positive relationships among instrumental attitude, injunctive norm, perceived control, self-efficacy and intention to save. Secondly, intention to save, financial literacy and time preference were found to positively influence saving behaviour.

Policymakers may find this study useful as the results reveal saving behaviour determinants of Gen Ys in Malaysia, and policies could then be formulated to improve Gen Y's saving behaviour.

This study contributes to the literature by applying the IBM to a study on saving behaviour.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-05-2023-0340

The main objective of this study is to evaluate the driving forces behind the adoption of indoor environmental quality (IEQ) principles in building designs from the perspectives of Nigerian quantity surveying firms.

A quantitative approach was used which involved administering a well-structured questionnaire to a sample of 114 quantity surveyors. The collected data were analyzed using various statistical methods, including frequencies, percentages, mean item scores, Kruskal–Wallis test and exploratory factor analysis.

The top five ranked drivers were climate change mitigation, conservation of natural resources, reduction of waste and pollution, use of sustainable building materials and development of new materials and building systems. Based on the factor analysis, the study identified five clusters of drivers: (1) health and well-being drivers (2) economic drivers (3) environmental drivers (4) innovation and technology drivers and (5) regulatory drivers.

The findings from this study suggest that to effectively integrate IEQ principles, quantity surveying firms should consider developing comprehensive guidelines and checklists that align with the identified drivers and clustered categories. These resources can serve as practical tools for project teams, facilitating a structured and holistic approach to the incorporation of IEQ factors throughout the project lifecycle.

The study’s identification of the top drivers and the subsequent clustering of these drivers into five distinct categories contributes to the existing body of knowledge on IEQ. This approach provides a structured framework for comprehensively understanding the factors influencing IEQ adoption, offering a valuable tool for researchers, policymakers and industry practitioners.

Occupant behavior can lead to considerable uncertainties in thermal comfort and air quality within buildings. To tackle this challenge, the use of probabilistic controls to simulate occupant behavior has emerged as a potential solution. This study seeks to analyze the performance of free-running households by examining adaptive thermal comfort and CO2 concentration, both crucial variables in indoor air quality. The investigation of indoor environment dynamics caused by the occupants' behavior, especially after the COVID-19 pandemic, became increasingly important. Specifically, it investigates 13 distinct window and shading control strategies in courtyard houses to identify the factors that prompt occupants to interact with shading and windows and determine which control approach effectively minimizes the performance gap.

This paper compares commonly used deterministic and probabilistic control functions and their effects on occupant comfort and indoor air quality in four zones surrounding a courtyard. The zones are differentiated by windows facing the courtyard. The study utilizes the energy management system (EMS) functionality of EnergyPlus within an algorithmic interface called Ladybug Tools. By modifying geometrical dimensions, orientation, window-to-wall ratio (WWR) and window operable fraction, a total of 465 cases are analyzed to identify effective control scenarios. According to the literature, these factors were selected because of their potential significant impact on occupants’ thermal comfort and indoor air quality, in addition to the natural ventilation flow rate. Additionally, the Random Forest algorithm is employed to estimate the individual impact of each control scenario on indoor thermal comfort and air quality metrics, including operative temperature and CO2 concentration.

The findings of the study confirmed that both deterministic and probabilistic window control algorithms were effective in reducing thermal discomfort hours, with reductions of 56.7 and 41.1%, respectively. Deterministic shading controls resulted in a reduction of 18.5%. Implementing the window control strategies led to a significant decrease of 87.8% in indoor CO2 concentration. The sensitivity analysis revealed that outdoor temperature exhibited the strongest positive correlation with indoor operative temperature while showing a negative correlation with indoor CO2 concentration. Furthermore, zone orientation and length were identified as the most influential design variables in achieving the desired performance outcomes.

It’s important to acknowledge the limitations of this study. Firstly, the potential impact of air circulation through the central zone was not considered. Secondly, the investigated control scenarios may have different impacts on air-conditioned buildings, especially when considering energy consumption. Thirdly, the study heavily relied on simulation tools and algorithms, which may limit its real-world applicability. The accuracy of the simulations depends on the quality of the input data and the assumptions made in the models. Fourthly, the case study is hypothetical in nature to be able to compare different control scenarios and their implications. Lastly, the comparative analysis was limited to a specific climate, which may restrict the generalizability of the findings in different climates.

Occupant behavior represents a significant source of uncertainty, particularly during the early stages of design. This study aims to offer a comparative analysis of various deterministic and probabilistic control scenarios that are based on occupant behavior. The study evaluates the effectiveness and validity of these proposed control scenarios, providing valuable insights for design decision-making.

Financial technology (FinTech) has enhanced the inclusivity and accessibility of traditional finance, offering a novel pathway for rural revitalization and development. The paper aims to assess the rural revitalization development level in prefecture-level cities in China and investigate the potential impact mechanism of FinTech.

This paper develops an index system to evaluate the rural revitalization level across 279 cities in China from 2011 to 2021. In addition, multi-mediation and threshold models are employed to analyze how FinTech influences rural revitalization.

The results reveal that, first, FinTech has significantly promoted rural revitalization. Second, entrepreneurial activeness, innovation capability, and industrial structure advancement are intermediary factors within the benchmark path. Third, FinTech exhibits varied threshold effects in entrepreneurial activeness, innovation capability, and industrial structure advancement, influencing rural revitalization with diverse impacts.

First, this paper expands the rural revitalization evaluation to include 30 indexes, enhancing overall measurement comprehensiveness. Second, in contrast to previous research concentrating on provincial-level assessments, this paper explores rural revitalization across 279 cities in China from 2011 to 2021, broadening the study’s scope and timeline. Third, this paper delves into empirical evidence illustrating how FinTech contributes to rural revitalization through entrepreneurial activeness, urban innovation capability, and industrial structure advancement, thereby deepening research in this domain.